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Systematic Reviews: Step 7: Extract Data from Included Studies

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  • Step 1: Complete Pre-Review Tasks
  • Step 2: Develop a Protocol
  • Step 3: Conduct Literature Searches
  • Step 4: Manage Citations
  • Step 5: Screen Citations
  • Step 6: Assess Quality of Included Studies

About Step 7: Extract Data from Included Studies

About data extraction, select a data extraction tool, what should i extract, helpful tip- data extraction.

  • Data extraction FAQs
  • Step 8: Write the Review

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In Step 7, you will skim the full text of included articles to collect information about the studies in a table format (extract data), to summarize the studies and make them easier to compare. You will: 

  • Make sure you have collected the full text of any included articles.
  • Choose the pieces of information you want to collect from each study.
  • Choose a method for collecting the data.
  • Create the data extraction table.
  • Test the data collection table (optional). 
  • Collect (extract) the data. 
  • Review the data collected for any errors. 

For accuracy, two or more people should extract data from each study. This process can be done by hand or by using a computer program. 

Click an item below to see how it applies to Step 7: Extract Data from Included Studies.

Reporting your review with PRISMA

If you reach the data extraction step and choose to exclude articles for any reason, update the number of included and excluded studies in your PRISMA flow diagram.

Managing your review with Covidence

Covidence allows you to assemble a custom data extraction template, have two reviewers conduct extraction, then send their extractions for consensus.

How a librarian can help with Step 7

A librarian can advise you on data extraction for your systematic review, including: 

  • What the data extraction stage of the review entails
  • Finding examples in the literature of similar reviews and their completed data tables
  • How to choose what data to extract from your included articles 
  • How to create a randomized sample of citations for a pilot test
  • Best practices for reporting your included studies and their important data in your review

In this step of the systematic review, you will develop your evidence tables, which give detailed information for each study (perhaps using a PICO framework as a guide), and summary tables, which give a high-level overview of the findings of your review. You can create evidence and summary tables to describe study characteristics, results, or both. These tables will help you determine which studies, if any, are eligible for quantitative synthesis.

Data extraction requires a lot of planning.  We will review some of the tools you can use for data extraction, the types of information you will want to extract, and the options available in the systematic review software used here at UNC, Covidence .

How many people should extract data?

The Cochrane Handbook and other studies strongly suggest at least two reviewers and extractors to reduce the number of errors.

  • Chapter 5: Collecting Data (Cochrane Handbook)
  • A Practical Guide to Data Extraction for Intervention Systematic Reviews (Covidence)

Click on a type of data extraction tool below to see some more information about using that type of tool and what UNC has to offer.

Systematic Review Software (Covidence)

Most systematic review software tools have data extraction functionality that can save you time and effort.  Here at UNC, we use a systematic review software called Covidence. You can see a more complete list of options in the Systematic Review Toolbox .

Covidence allows you to create and publish a data extraction template with text fields, single-choice items, section headings and section subheadings; perform dual and single reviewer data extraction ; review extractions for consensus ; and export data extraction and quality assessment to a CSV with each item in a column and each study in a row.

  • Covidence@UNC Guide
  • Covidence for Data Extraction (Covidence)
  • A Practical Guide to Data Extraction for Intervention Systematic Reviews(Covidence)

Spreadsheet or Database Software (Excel, Google Sheets)

You can also use spreadsheet or database software to create custom extraction forms. Spreadsheet software (such as Microsoft Excel) has functions such as drop-down menus and range checks can speed up the process and help prevent data entry errors. Relational databases (such as Microsoft Access) can help you extract information from different categories like citation details, demographics, participant selection, intervention, outcomes, etc.

  • Microsoft Products (UNC Information Technology Services)

Cochrane RevMan

RevMan offers collection forms for descriptive information on population, interventions, and outcomes, and quality assessments, as well as for data for analysis and forest plots. The form elements may not be changed, and data must be entered manually. RevMan is a free software download.

  • Cochrane RevMan 5.0 Download
  • RevMan for Non-Cochrane Reviews (Cochrane Training)

Survey or Form Software (Qualtrics, Poll Everywhere)

Survey or form tools can help you create custom forms with many different question types, such as multiple choice, drop downs, ranking, and more. Content from these tools can often be exported to spreadsheet or database software as well. Here at UNC we have access to the survey/form software Qualtrics & Poll Everywhere.

  • Qualtrics (UNC Information Technology Services)
  • Poll Everywhere (UNC Information Technology Services)

Electronic Documents or Paper & Pencil (Word, Google Docs)

In the past, people often used paper and pencil to record the data they extracted from articles. Handwritten extraction is less popular now due to widespread electronic tools. You can record extracted data in electronic tables or forms created in Microsoft Word or other word processing programs, but this process may take longer than many of our previously listed methods. If chosen, the electronic document or paper-and-pencil extraction methods should only be used for small reviews, as larger sets of articles may become unwieldy. These methods may also be more prone to errors in data entry than some of the more automated methods.

There are benefits and limitations to each method of data extraction.  You will want to consider:

  • The cost of the software / tool
  • Shareability / versioning
  • Existing versus custom data extraction forms
  • The data entry process
  • Interrater reliability

For example, in Covidence you may spend more time building your data extraction form, but save time later in the extraction process as Covidence can automatically highlight discrepancies for review and resolution between different extractors. Excel may require less time investment to create an extraction form, but it may take longer for you to match and compare data between extractors. More in-depth comparison of the benefits and limitations of each extraction tool can be found in the table below.

Sample information to include in an extraction table

It may help to consult other similar systematic reviews to identify what data to collect or to think about your question in a framework such as PICO .

Helpful data for an intervention question may include:

  • Information about the article (author(s), year of publication, title, DOI)
  • Information about the study (study type, participant recruitment / selection / allocation, level of evidence, study quality)
  • Patient demographics (age, sex, ethnicity, diseases / conditions, other characteristics related to the intervention / outcome)
  • Intervention (quantity, dosage, route of administration, format, duration, time frame, setting)
  • Outcomes (quantitative and / or qualitative)

If you plan to synthesize data, you will want to collect additional information such as sample sizes, effect sizes, dependent variables, reliability measures, pre-test data, post-test data, follow-up data, and statistical tests used.

Extraction templates and approaches should be determined by the needs of the specific review.   For example, if you are extracting qualitative data, you will want to extract data such as theoretical framework, data collection method, or role of the researcher and their potential bias.

  • Supplementary Guidance for Inclusion of Qualitative Research in Cochrane Systematic Reviews of Interventions (Cochrane Collaboration Qualitative Methods Group)
  • Look for an existing extraction form or tool to help guide you.  Use existing systematic reviews on your topic to identify what information to collect if you are not sure what to do.
  • Train the review team on the extraction categories and what type of data would be expected.  A manual or guide may help your team establish standards.
  • Pilot the extraction / coding form to ensure data extractors are recording similar data. Revise the extraction form if needed.
  • Discuss any discrepancies in coding throughout the process.
  • Document any changes to the process or the form.  Keep track of the decisions the team makes and the reasoning behind them.
  • << Previous: Step 6: Assess Quality of Included Studies
  • Next: Step 8: Write the Review >>
  • Last Updated: Feb 8, 2024 9:22 AM
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4.6.1   Characteristics of included studies

The ‘Characteristics of included studies’ table has five entries for each study: Methods, Participants, Interventions, Outcomes and Notes. Up to three further entries may be specified for items not conveniently covered by these categories, for example, to provide information on length of follow-up, funding source, or indications of study quality that are unlikely to lead directly to a risk of bias (see Section 4.6.2 for including information on the risk of bias). Codes or abbreviations may be used in the table to enable clear and succinct presentation of multiple pieces of information within an entry; for example, authors could include country, setting, age and sex under the Participants entry. Footnotes should be used to explain any codes or abbreviations used (these will be published in the CDSR ).

Detailed guidance on ‘Characteristics of included studies’ tables is provided in Chapter 11 (Section 11.2.2 ).

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Methodology

  • Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

  • Formulate a research question
  • Develop a protocol
  • Search for all relevant studies
  • Apply the selection criteria
  • Extract the data
  • Synthesize the data
  • Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

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Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

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A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

  • A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
  • If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
  • Access to databases and journal archives. Often, your educational institution provides you with access.
  • Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
  • Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

  • They minimize research bias by considering all available evidence and evaluating each study for bias.
  • Their methods are transparent , so they can be scrutinized by others.
  • They’re thorough : they summarize all available evidence.
  • They can be replicated and updated by others.

Systematic reviews also have a few cons .

  • They’re time-consuming .
  • They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

  • Allow you to more effectively communicate your research to other researchers and practitioners
  • Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

  • Population(s) or problem(s)
  • Intervention(s)
  • Comparison(s)

You can rearrange these four components to write your research question:

  • What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

  • Type of study design(s)
  • The population of patients with eczema
  • The intervention of probiotics
  • In comparison to no treatment, placebo , or non-probiotic treatment
  • The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
  • Randomized control trials, a type of study design

Their research question was:

  • What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

  • Background information : Provide the context of the research question, including why it’s important.
  • Research objective (s) : Rephrase your research question as an objective.
  • Selection criteria: State how you’ll decide which studies to include or exclude from your review.
  • Search strategy: Discuss your plan for finding studies.
  • Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

  • Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
  • Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
  • Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
  • Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

  • Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
  • Handsearch: Conference proceedings and reference lists of articles
  • Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
  • Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

  • Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
  • Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

  • Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
  • Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

  • Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
  • Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

  • Abstract : A summary of the review
  • Introduction : Including the rationale and objectives
  • Methods : Including the selection criteria, search method, data extraction method, and synthesis method
  • Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
  • Discussion : Including interpretation of the results and limitations of the review
  • Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Student’s  t -distribution
  • Normal distribution
  • Null and Alternative Hypotheses
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Data cleansing
  • Reproducibility vs Replicability
  • Peer review
  • Prospective cohort study

Research bias

  • Implicit bias
  • Cognitive bias
  • Placebo effect
  • Hawthorne effect
  • Hindsight bias
  • Affect heuristic
  • Social desirability bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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  • Open access
  • Published: 29 March 2021

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

  • Matthew J. Page   ORCID: orcid.org/0000-0002-4242-7526 1 ,
  • Joanne E. McKenzie 1 ,
  • Patrick M. Bossuyt 2 ,
  • Isabelle Boutron 3 ,
  • Tammy C. Hoffmann 4 ,
  • Cynthia D. Mulrow 5 ,
  • Larissa Shamseer 6 ,
  • Jennifer M. Tetzlaff 7 ,
  • Elie A. Akl 8 ,
  • Sue E. Brennan 1 ,
  • Roger Chou 9 ,
  • Julie Glanville 10 ,
  • Jeremy M. Grimshaw 11 ,
  • Asbjørn Hróbjartsson 12 ,
  • Manoj M. Lalu 13 ,
  • Tianjing Li 14 ,
  • Elizabeth W. Loder 15 ,
  • Evan Mayo-Wilson 16 ,
  • Steve McDonald 1 ,
  • Luke A. McGuinness 17 ,
  • Lesley A. Stewart 18 ,
  • James Thomas 19 ,
  • Andrea C. Tricco 20 ,
  • Vivian A. Welch 21 ,
  • Penny Whiting 17 &
  • David Moher 22  

Systematic Reviews volume  10 , Article number:  89 ( 2021 ) Cite this article

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An Editorial to this article was published on 19 April 2021

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews. In order to encourage its wide dissemination this article is freely accessible on BMJ, PLOS Medicine, Journal of Clinical Epidemiology and International Journal of Surgery journal websites.

Systematic reviews serve many critical roles. They can provide syntheses of the state of knowledge in a field, from which future research priorities can be identified; they can address questions that otherwise could not be answered by individual studies; they can identify problems in primary research that should be rectified in future studies; and they can generate or evaluate theories about how or why phenomena occur. Systematic reviews therefore generate various types of knowledge for different users of reviews (such as patients, healthcare providers, researchers, and policy makers) [ 1 , 2 ]. To ensure a systematic review is valuable to users, authors should prepare a transparent, complete, and accurate account of why the review was done, what they did (such as how studies were identified and selected) and what they found (such as characteristics of contributing studies and results of meta-analyses). Up-to-date reporting guidance facilitates authors achieving this [ 3 ].

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement published in 2009 (hereafter referred to as PRISMA 2009) [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ] is a reporting guideline designed to address poor reporting of systematic reviews [ 11 ]. The PRISMA 2009 statement comprised a checklist of 27 items recommended for reporting in systematic reviews and an “explanation and elaboration” paper [ 12 , 13 , 14 , 15 , 16 ] providing additional reporting guidance for each item, along with exemplars of reporting. The recommendations have been widely endorsed and adopted, as evidenced by its co-publication in multiple journals, citation in over 60,000 reports (Scopus, August 2020), endorsement from almost 200 journals and systematic review organisations, and adoption in various disciplines. Evidence from observational studies suggests that use of the PRISMA 2009 statement is associated with more complete reporting of systematic reviews [ 17 , 18 , 19 , 20 ], although more could be done to improve adherence to the guideline [ 21 ].

Many innovations in the conduct of systematic reviews have occurred since publication of the PRISMA 2009 statement. For example, technological advances have enabled the use of natural language processing and machine learning to identify relevant evidence [ 22 , 23 , 24 ], methods have been proposed to synthesise and present findings when meta-analysis is not possible or appropriate [ 25 , 26 , 27 ], and new methods have been developed to assess the risk of bias in results of included studies [ 28 , 29 ]. Evidence on sources of bias in systematic reviews has accrued, culminating in the development of new tools to appraise the conduct of systematic reviews [ 30 , 31 ]. Terminology used to describe particular review processes has also evolved, as in the shift from assessing “quality” to assessing “certainty” in the body of evidence [ 32 ]. In addition, the publishing landscape has transformed, with multiple avenues now available for registering and disseminating systematic review protocols [ 33 , 34 ], disseminating reports of systematic reviews, and sharing data and materials, such as preprint servers and publicly accessible repositories. To capture these advances in the reporting of systematic reviews necessitated an update to the PRISMA 2009 statement.

Development of PRISMA 2020

A complete description of the methods used to develop PRISMA 2020 is available elsewhere [ 35 ]. We identified PRISMA 2009 items that were often reported incompletely by examining the results of studies investigating the transparency of reporting of published reviews [ 17 , 21 , 36 , 37 ]. We identified possible modifications to the PRISMA 2009 statement by reviewing 60 documents providing reporting guidance for systematic reviews (including reporting guidelines, handbooks, tools, and meta-research studies) [ 38 ]. These reviews of the literature were used to inform the content of a survey with suggested possible modifications to the 27 items in PRISMA 2009 and possible additional items. Respondents were asked whether they believed we should keep each PRISMA 2009 item as is, modify it, or remove it, and whether we should add each additional item. Systematic review methodologists and journal editors were invited to complete the online survey (110 of 220 invited responded). We discussed proposed content and wording of the PRISMA 2020 statement, as informed by the review and survey results, at a 21-member, two-day, in-person meeting in September 2018 in Edinburgh, Scotland. Throughout 2019 and 2020, we circulated an initial draft and five revisions of the checklist and explanation and elaboration paper to co-authors for feedback. In April 2020, we invited 22 systematic reviewers who had expressed interest in providing feedback on the PRISMA 2020 checklist to share their views (via an online survey) on the layout and terminology used in a preliminary version of the checklist. Feedback was received from 15 individuals and considered by the first author, and any revisions deemed necessary were incorporated before the final version was approved and endorsed by all co-authors.

The PRISMA 2020 statement

Scope of the guideline.

The PRISMA 2020 statement has been designed primarily for systematic reviews of studies that evaluate the effects of health interventions, irrespective of the design of the included studies. However, the checklist items are applicable to reports of systematic reviews evaluating other interventions (such as social or educational interventions), and many items are applicable to systematic reviews with objectives other than evaluating interventions (such as evaluating aetiology, prevalence, or prognosis). PRISMA 2020 is intended for use in systematic reviews that include synthesis (such as pairwise meta-analysis or other statistical synthesis methods) or do not include synthesis (for example, because only one eligible study is identified). The PRISMA 2020 items are relevant for mixed-methods systematic reviews (which include quantitative and qualitative studies), but reporting guidelines addressing the presentation and synthesis of qualitative data should also be consulted [ 39 , 40 ]. PRISMA 2020 can be used for original systematic reviews, updated systematic reviews, or continually updated (“living”) systematic reviews. However, for updated and living systematic reviews, there may be some additional considerations that need to be addressed. Where there is relevant content from other reporting guidelines, we reference these guidelines within the items in the explanation and elaboration paper [ 41 ] (such as PRISMA-Search [ 42 ] in items 6 and 7, Synthesis without meta-analysis (SWiM) reporting guideline [ 27 ] in item 13d). Box 1 includes a glossary of terms used throughout the PRISMA 2020 statement.

PRISMA 2020 is not intended to guide systematic review conduct, for which comprehensive resources are available [ 43 , 44 , 45 , 46 ]. However, familiarity with PRISMA 2020 is useful when planning and conducting systematic reviews to ensure that all recommended information is captured. PRISMA 2020 should not be used to assess the conduct or methodological quality of systematic reviews; other tools exist for this purpose [ 30 , 31 ]. Furthermore, PRISMA 2020 is not intended to inform the reporting of systematic review protocols, for which a separate statement is available (PRISMA for Protocols (PRISMA-P) 2015 statement [ 47 , 48 ]). Finally, extensions to the PRISMA 2009 statement have been developed to guide reporting of network meta-analyses [ 49 ], meta-analyses of individual participant data [ 50 ], systematic reviews of harms [ 51 ], systematic reviews of diagnostic test accuracy studies [ 52 ], and scoping reviews [ 53 ]; for these types of reviews we recommend authors report their review in accordance with the recommendations in PRISMA 2020 along with the guidance specific to the extension.

How to use PRISMA 2020

The PRISMA 2020 statement (including the checklists, explanation and elaboration, and flow diagram) replaces the PRISMA 2009 statement, which should no longer be used. Box  2 summarises noteworthy changes from the PRISMA 2009 statement. The PRISMA 2020 checklist includes seven sections with 27 items, some of which include sub-items (Table  1 ). A checklist for journal and conference abstracts for systematic reviews is included in PRISMA 2020. This abstract checklist is an update of the 2013 PRISMA for Abstracts statement [ 54 ], reflecting new and modified content in PRISMA 2020 (Table  2 ). A template PRISMA flow diagram is provided, which can be modified depending on whether the systematic review is original or updated (Fig.  1 ).

figure 1

 PRISMA 2020 flow diagram template for systematic reviews. The new design is adapted from flow diagrams proposed by Boers [ 55 ], Mayo-Wilson et al. [ 56 ] and Stovold et al. [ 57 ] The boxes in grey should only be completed if applicable; otherwise they should be removed from the flow diagram. Note that a “report” could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report or any other document providing relevant information

We recommend authors refer to PRISMA 2020 early in the writing process, because prospective consideration of the items may help to ensure that all the items are addressed. To help keep track of which items have been reported, the PRISMA statement website ( http://www.prisma-statement.org/ ) includes fillable templates of the checklists to download and complete (also available in Additional file 1 ). We have also created a web application that allows users to complete the checklist via a user-friendly interface [ 58 ] (available at https://prisma.shinyapps.io/checklist/ and adapted from the Transparency Checklist app [ 59 ]). The completed checklist can be exported to Word or PDF. Editable templates of the flow diagram can also be downloaded from the PRISMA statement website.

We have prepared an updated explanation and elaboration paper, in which we explain why reporting of each item is recommended and present bullet points that detail the reporting recommendations (which we refer to as elements) [ 41 ]. The bullet-point structure is new to PRISMA 2020 and has been adopted to facilitate implementation of the guidance [ 60 , 61 ]. An expanded checklist, which comprises an abridged version of the elements presented in the explanation and elaboration paper, with references and some examples removed, is available in Additional file 2 . Consulting the explanation and elaboration paper is recommended if further clarity or information is required.

Journals and publishers might impose word and section limits, and limits on the number of tables and figures allowed in the main report. In such cases, if the relevant information for some items already appears in a publicly accessible review protocol, referring to the protocol may suffice. Alternatively, placing detailed descriptions of the methods used or additional results (such as for less critical outcomes) in supplementary files is recommended. Ideally, supplementary files should be deposited to a general-purpose or institutional open-access repository that provides free and permanent access to the material (such as Open Science Framework, Dryad, figshare). A reference or link to the additional information should be included in the main report. Finally, although PRISMA 2020 provides a template for where information might be located, the suggested location should not be seen as prescriptive; the guiding principle is to ensure the information is reported.

Use of PRISMA 2020 has the potential to benefit many stakeholders. Complete reporting allows readers to assess the appropriateness of the methods, and therefore the trustworthiness of the findings. Presenting and summarising characteristics of studies contributing to a synthesis allows healthcare providers and policy makers to evaluate the applicability of the findings to their setting. Describing the certainty in the body of evidence for an outcome and the implications of findings should help policy makers, managers, and other decision makers formulate appropriate recommendations for practice or policy. Complete reporting of all PRISMA 2020 items also facilitates replication and review updates, as well as inclusion of systematic reviews in overviews (of systematic reviews) and guidelines, so teams can leverage work that is already done and decrease research waste [ 36 , 62 , 63 ].

We updated the PRISMA 2009 statement by adapting the EQUATOR Network’s guidance for developing health research reporting guidelines [ 64 ]. We evaluated the reporting completeness of published systematic reviews [ 17 , 21 , 36 , 37 ], reviewed the items included in other documents providing guidance for systematic reviews [ 38 ], surveyed systematic review methodologists and journal editors for their views on how to revise the original PRISMA statement [ 35 ], discussed the findings at an in-person meeting, and prepared this document through an iterative process. Our recommendations are informed by the reviews and survey conducted before the in-person meeting, theoretical considerations about which items facilitate replication and help users assess the risk of bias and applicability of systematic reviews, and co-authors’ experience with authoring and using systematic reviews.

Various strategies to increase the use of reporting guidelines and improve reporting have been proposed. They include educators introducing reporting guidelines into graduate curricula to promote good reporting habits of early career scientists [ 65 ]; journal editors and regulators endorsing use of reporting guidelines [ 18 ]; peer reviewers evaluating adherence to reporting guidelines [ 61 , 66 ]; journals requiring authors to indicate where in their manuscript they have adhered to each reporting item [ 67 ]; and authors using online writing tools that prompt complete reporting at the writing stage [ 60 ]. Multi-pronged interventions, where more than one of these strategies are combined, may be more effective (such as completion of checklists coupled with editorial checks) [ 68 ]. However, of 31 interventions proposed to increase adherence to reporting guidelines, the effects of only 11 have been evaluated, mostly in observational studies at high risk of bias due to confounding [ 69 ]. It is therefore unclear which strategies should be used. Future research might explore barriers and facilitators to the use of PRISMA 2020 by authors, editors, and peer reviewers, designing interventions that address the identified barriers, and evaluating those interventions using randomised trials. To inform possible revisions to the guideline, it would also be valuable to conduct think-aloud studies [ 70 ] to understand how systematic reviewers interpret the items, and reliability studies to identify items where there is varied interpretation of the items.

We encourage readers to submit evidence that informs any of the recommendations in PRISMA 2020 (via the PRISMA statement website: http://www.prisma-statement.org/ ). To enhance accessibility of PRISMA 2020, several translations of the guideline are under way (see available translations at the PRISMA statement website). We encourage journal editors and publishers to raise awareness of PRISMA 2020 (for example, by referring to it in journal “Instructions to authors”), endorsing its use, advising editors and peer reviewers to evaluate submitted systematic reviews against the PRISMA 2020 checklists, and making changes to journal policies to accommodate the new reporting recommendations. We recommend existing PRISMA extensions [ 47 , 49 , 50 , 51 , 52 , 53 , 71 , 72 ] be updated to reflect PRISMA 2020 and advise developers of new PRISMA extensions to use PRISMA 2020 as the foundation document.

We anticipate that the PRISMA 2020 statement will benefit authors, editors, and peer reviewers of systematic reviews, and different users of reviews, including guideline developers, policy makers, healthcare providers, patients, and other stakeholders. Ultimately, we hope that uptake of the guideline will lead to more transparent, complete, and accurate reporting of systematic reviews, thus facilitating evidence based decision making.

Box 1 Glossary of terms

Systematic review —A review that uses explicit, systematic methods to collate and synthesise findings of studies that address a clearly formulated question [ 43 ]

Statistical synthesis —The combination of quantitative results of two or more studies. This encompasses meta-analysis of effect estimates (described below) and other methods, such as combining P values, calculating the range and distribution of observed effects, and vote counting based on the direction of effect (see McKenzie and Brennan [ 25 ] for a description of each method)

Meta-analysis of effect estimates —A statistical technique used to synthesise results when study effect estimates and their variances are available, yielding a quantitative summary of results [ 25 ]

Outcome —An event or measurement collected for participants in a study (such as quality of life, mortality)

Result —The combination of a point estimate (such as a mean difference, risk ratio, or proportion) and a measure of its precision (such as a confidence/credible interval) for a particular outcome

Report —A document (paper or electronic) supplying information about a particular study. It could be a journal article, preprint, conference abstract, study register entry, clinical study report, dissertation, unpublished manuscript, government report, or any other document providing relevant information

Record —The title or abstract (or both) of a report indexed in a database or website (such as a title or abstract for an article indexed in Medline). Records that refer to the same report (such as the same journal article) are “duplicates”; however, records that refer to reports that are merely similar (such as a similar abstract submitted to two different conferences) should be considered unique.

Study —An investigation, such as a clinical trial, that includes a defined group of participants and one or more interventions and outcomes. A “study” might have multiple reports. For example, reports could include the protocol, statistical analysis plan, baseline characteristics, results for the primary outcome, results for harms, results for secondary outcomes, and results for additional mediator and moderator analyses

Box 2 Noteworthy changes to the PRISMA 2009 statement

• Inclusion of the abstract reporting checklist within PRISMA 2020 (see item #2 and Box 2 ).

• Movement of the ‘Protocol and registration’ item from the start of the Methods section of the checklist to a new Other section, with addition of a sub-item recommending authors describe amendments to information provided at registration or in the protocol (see item #24a-24c).

• Modification of the ‘Search’ item to recommend authors present full search strategies for all databases, registers and websites searched, not just at least one database (see item #7).

• Modification of the ‘Study selection’ item in the Methods section to emphasise the reporting of how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process (see item #8).

• Addition of a sub-item to the ‘Data items’ item recommending authors report how outcomes were defined, which results were sought, and methods for selecting a subset of results from included studies (see item #10a).

• Splitting of the ‘Synthesis of results’ item in the Methods section into six sub-items recommending authors describe: the processes used to decide which studies were eligible for each synthesis; any methods required to prepare the data for synthesis; any methods used to tabulate or visually display results of individual studies and syntheses; any methods used to synthesise results; any methods used to explore possible causes of heterogeneity among study results (such as subgroup analysis, meta-regression); and any sensitivity analyses used to assess robustness of the synthesised results (see item #13a-13f).

• Addition of a sub-item to the ‘Study selection’ item in the Results section recommending authors cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded (see item #16b).

• Splitting of the ‘Synthesis of results’ item in the Results section into four sub-items recommending authors: briefly summarise the characteristics and risk of bias among studies contributing to the synthesis; present results of all statistical syntheses conducted; present results of any investigations of possible causes of heterogeneity among study results; and present results of any sensitivity analyses (see item #20a-20d).

• Addition of new items recommending authors report methods for and results of an assessment of certainty (or confidence) in the body of evidence for an outcome (see items #15 and #22).

• Addition of a new item recommending authors declare any competing interests (see item #26).

• Addition of a new item recommending authors indicate whether data, analytic code and other materials used in the review are publicly available and if so, where they can be found (see item #27).

Gurevitch J, Koricheva J, Nakagawa S, Stewart G. Meta-analysis and the science of research synthesis. Nature. 2018;555:175–82. https://doi.org/10.1038/nature25753 .

Article   CAS   PubMed   Google Scholar  

Gough D, Thomas J, Oliver S. Clarifying differences between reviews within evidence ecosystems. Syst Rev. 2019;8:170. https://doi.org/10.1186/s13643-019-1089-2 .

Article   PubMed   PubMed Central   Google Scholar  

Moher D. Reporting guidelines: doing better for readers. BMC Med. 2018;16:233. https://doi.org/10.1186/s12916-018-1226-0 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264–9, W64. https://doi.org/10.7326/0003-4819-151-4-200908180-00135 .

Article   PubMed   Google Scholar  

Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535. https://doi.org/10.1136/bmj.b2535 .

Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097. https://doi.org/10.1371/journal.pmed.1000097 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62:1006–12. https://doi.org/10.1016/j.jclinepi.2009.06.005 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8:336–41. https://doi.org/10.1016/j.ijsu.2010.02.007 .

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med. 2009;3:e123–30.

PubMed   PubMed Central   Google Scholar  

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Reprint--preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Phys Ther. 2009;89:873–80. https://doi.org/10.1093/ptj/89.9.873 .

Moher D, Tetzlaff J, Tricco AC, Sampson M, Altman DG. Epidemiology and reporting characteristics of systematic reviews. PLoS Med. 2007;4:e78. https://doi.org/10.1371/journal.pmed.0040078 .

Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62:e1–34. https://doi.org/10.1016/j.jclinepi.2009.06.006 .

Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. https://doi.org/10.1136/bmj.b2700 .

Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Ann Intern Med. 2009;151:W65–94. https://doi.org/10.7326/0003-4819-151-4-200908180-00136 .

Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 2009;6:e1000100. https://doi.org/10.1371/journal.pmed.1000100 .

Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting. systematic reviews and meta-analyses of studies that evaluate health care. interventions: explanation and elaboration. PLoS Med. 2009;6:e1000100. https://doi.org/10.1371/journal.pmed.1000100 .

Page MJ, Shamseer L, Altman DG, et al. Epidemiology and reporting characteristics of systematic reviews of biomedical research: a cross-sectional study. PLoS Med. 2016;13:e1002028. https://doi.org/10.1371/journal.pmed.1002028 .

Panic N, Leoncini E, de Belvis G, Ricciardi W, Boccia S. Evaluation of the endorsement of the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement on the quality of published systematic review and meta-analyses. PLoS One. 2013;8:e83138. https://doi.org/10.1371/journal.pone.0083138 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Agha RA, Fowler AJ, Limb C, et al. Impact of the mandatory implementation of reporting guidelines on reporting quality in a surgical journal: a before and after study. Int J Surg. 2016;30:169–72. https://doi.org/10.1016/j.ijsu.2016.04.032 .

Leclercq V, Beaudart C, Ajamieh S, Rabenda V, Tirelli E, Bruyère O. Meta-analyses indexed in PsycINFO had a better completeness of reporting when they mention PRISMA. J Clin Epidemiol. 2019;115:46–54. https://doi.org/10.1016/j.jclinepi.2019.06.014 .

Page MJ, Moher D. Evaluations of the uptake and impact of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and extensions: a scoping review. Syst Rev. 2017;6:263. https://doi.org/10.1186/s13643-017-0663-8 .

O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4:5. https://doi.org/10.1186/2046-4053-4-5 .

Marshall IJ, Noel-Storr A, Kuiper J, Thomas J, Wallace BC. Machine learning for identifying randomized controlled trials: an evaluation and practitioner’s guide. Res Synth Methods. 2018;9:602–14. https://doi.org/10.1002/jrsm.1287 .

Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev. 2019;8:163. https://doi.org/10.1186/s13643-019-1074-9 .

McKenzie JE, Brennan SE. Synthesizing and presenting findings using other methods. In: Higgins JPT, Thomas J, Chandler J, et al., editors. Cochrane handbook for systematic reviews of interventions. London: Cochrane; 2019. https://doi.org/10.1002/9781119536604.ch12 .

Chapter   Google Scholar  

Higgins JPT, López-López JA, Becker BJ, et al. Synthesising quantitative evidence in systematic reviews of complex health interventions. BMJ Glob Health. 2019;4(Suppl 1):e000858. https://doi.org/10.1136/bmjgh-2018-000858 .

Campbell M, McKenzie JE, Sowden A, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;368:l6890. https://doi.org/10.1136/bmj.l6890 .

Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. https://doi.org/10.1136/bmj.l4898 .

Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. https://doi.org/10.1136/bmj.i4919 .

Whiting P, Savović J, Higgins JP, ROBIS group, et al. ROBIS: a new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol. 2016;69:225–34. https://doi.org/10.1016/j.jclinepi.2015.06.005 .

Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008. https://doi.org/10.1136/bmj.j4008 .

Hultcrantz M, Rind D, Akl EA, et al. The GRADE working group clarifies the construct of certainty of evidence. J Clin Epidemiol. 2017;87:4–13. https://doi.org/10.1016/j.jclinepi.2017.05.006 .

Booth A, Clarke M, Dooley G, et al. The nuts and bolts of PROSPERO: an international prospective register of systematic reviews. Syst Rev. 2012;1:2. https://doi.org/10.1186/2046-4053-1-2 .

Moher D, Stewart L, Shekelle P. Establishing a new journal for systematic review products. Syst Rev. 2012;1:1. https://doi.org/10.1186/2046-4053-1-1 .

Page MJ, McKenzie JE, Bossuyt PM, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol 2021;134:103–112. https://doi.org/10.1016/j.jclinepi.2021.02.003 .

Page MJ, Altman DG, Shamseer L, et al. Reproducible research practices are underused in systematic reviews of biomedical interventions. J Clin Epidemiol. 2018;94:8–18. https://doi.org/10.1016/j.jclinepi.2017.10.017 .

Page MJ, Altman DG, McKenzie JE, et al. Flaws in the application and interpretation of statistical analyses in systematic reviews of therapeutic interventions were common: a cross-sectional analysis. J Clin Epidemiol. 2018;95:7–18. https://doi.org/10.1016/j.jclinepi.2017.11.022 .

Page MJ, McKenzie JE, Bossuyt PM, et al. Mapping of reporting guidance for systematic reviews and meta-analyses generated a comprehensive item bank for future reporting guidelines. J Clin Epidemiol. 2020;118:60–8. https://doi.org/10.1016/j.jclinepi.2019.11.010 .

Tong A, Flemming K, McInnes E, Oliver S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. 2012;12:181. https://doi.org/10.1186/1471-2288-12-181 .

France EF, Cunningham M, Ring N, et al. Improving reporting of meta-ethnography: the eMERGe reporting guidance. BMC Med Res Methodol. 2019;19:25. https://doi.org/10.1186/s12874-018-0600-0 .

Page MJ, Moher D, Bossuyt PM, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;372:n160. https://doi.org/10.1136/bmj.n160 .

Rethlefsen ML, Kirtley S, Waffenschmidt S, et al. PRISMA-S Group PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews. Syst Rev. 2021;10:39. https://doi.org/10.1186/s13643-020-01542-z .

Higgins JPT, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions: version 6.0. London: Cochrane; 2019. Available from https://training.cochrane.org/handbook

Book   Google Scholar  

Dekkers OM, Vandenbroucke JP, Cevallos M, Renehan AG, Altman DG, Egger M. COSMOS-E: guidance on conducting systematic reviews and meta-analyses of observational studies of etiology. PLoS Med. 2019;16:e1002742. https://doi.org/10.1371/journal.pmed.1002742 .

Cooper H, Hedges LV, Valentine JV. The handbook of research synthesis and meta-analysis. New York: Russell Sage Foundation; 2019.

IOM (Institute of Medicine). Finding what works in health care: standards for systematic reviews. Washington, D.C.: The National Academies Press; 2011.

Google Scholar  

Moher D, Shamseer L, Clarke M, PRISMA-P Group, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1. https://doi.org/10.1186/2046-4053-4-1 .

Shamseer L, Moher D, Clarke M, PRISMA-P Group, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;350:g7647. https://doi.org/10.1136/bmj.g7647 .

Hutton B, Salanti G, Caldwell DM, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162:777–84. https://doi.org/10.7326/M14-2385 .

Stewart LA, Clarke M, Rovers M, PRISMA-IPD Development Group, et al. Preferred reporting items for systematic review and meta-analyses of individual participant data: the PRISMA-IPD statement. JAMA. 2015;313:1657–65. https://doi.org/10.1001/jama.2015.3656 .

Zorzela L, Loke YK, Ioannidis JP, et al. PRISMAHarms Group PRISMA harms checklist: improving harms reporting in systematic reviews. BMJ. 2016;352:i157. https://doi.org/10.1136/bmj.i157 .

McInnes MDF, Moher D, Thombs BD, the PRISMA-DTA Group, et al. Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA. 2018;319:388–96. https://doi.org/10.1001/jama.2017.19163 .

Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-SCR): checklist and explanation. Ann Intern Med. 2018;169:467–73. https://doi.org/10.7326/M18-0850 .

Beller EM, Glasziou PP, Altman DG, et al. PRISMA for Abstracts Group PRISMA for Abstracts: reporting systematic reviews in journal and conference abstracts. PLoS Med. 2013;10:e1001419. https://doi.org/10.1371/journal.pmed.1001419 .

Boers M. Graphics and statistics for cardiology: designing effective tables for presentation and publication. Heart. 2018;104:192–200. https://doi.org/10.1136/heartjnl-2017-311581 .

Mayo-Wilson E, Li T, Fusco N, Dickersin K, MUDS investigators. Practical guidance for using multiple data sources in systematic reviews and meta-analyses (with examples from the MUDS study). Res Synth Methods. 2018;9:2–12. https://doi.org/10.1002/jrsm.1277 .

Stovold E, Beecher D, Foxlee R, Noel-Storr A. Study flow diagrams in Cochrane systematic review updates: an adapted PRISMA flow diagram. Syst Rev. 2014;3:54. https://doi.org/10.1186/2046-4053-3-54 .

McGuinness LA. mcguinlu/PRISMA-Checklist: Initial release for manuscript submission (Version v1.0.0). Geneva: Zenodo; 2020. https://doi.org/10.5281/zenodo.3994319 .

Aczel B, Szaszi B, Sarafoglou A, et al. A consensus-based transparency checklist. Nat Hum Behav. 2020;4:4–6. https://doi.org/10.1038/s41562-019-0772-6 .

Barnes C, Boutron I, Giraudeau B, Porcher R, Altman DG, Ravaud P. Impact of an online writing aid tool for writing a randomized trial report: the COBWEB (Consort-based WEB tool) randomized controlled trial. BMC Med. 2015;13:221. https://doi.org/10.1186/s12916-015-0460-y .

Chauvin A, Ravaud P, Moher D, et al. Accuracy in detecting inadequate research reporting by early career peer reviewers using an online CONSORT-based peer-review tool (COBPeer) versus the usual peer-review process: a cross-sectional diagnostic study. BMC Med. 2019;17:205. https://doi.org/10.1186/s12916-019-1436-0 .

Wayant C, Page MJ, Vassar M. Evaluation of reproducible research practices in oncology systematic reviews with meta-analyses referenced by national comprehensive cancer network guidelines. JAMA Oncol. 2019;5:1550–5. https://doi.org/10.1001/jamaoncol.2019.2564 .

Article   PubMed Central   PubMed   Google Scholar  

McKenzie JE, Brennan SE. Overviews of systematic reviews: great promise, greater challenge. Syst Rev. 2017;6:185. https://doi.org/10.1186/s13643-017-0582-8 .

Moher D, Schulz KF, Simera I, Altman DG. Guidance for developers of health research reporting guidelines. PLoS Med. 2010;7:e1000217. https://doi.org/10.1371/journal.pmed.1000217 .

Simera I, Moher D, Hirst A, Hoey J, Schulz KF, Altman DG. Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network. BMC Med. 2010;8:24. https://doi.org/10.1186/1741-7015-8-24 .

Speich B, Schroter S, Briel M, et al. Impact of a short version of the CONSORT checklist for peer reviewers to improve the reporting of randomised controlled trials published in biomedical journals: study protocol for a randomised controlled trial. BMJ Open. 2020;10:e035114. https://doi.org/10.1136/bmjopen-2019-035114 .

Stevens A, Shamseer L, Weinstein E, et al. Relation of completeness of reporting of health research to journals’ endorsement of reporting guidelines: systematic review. BMJ. 2014;348:g3804. https://doi.org/10.1136/bmj.g3804 .

Hair K, Macleod MR, Sena ES, IICARus Collaboration. A randomised controlled trial of an Intervention to Improve Compliance with the ARRIVE guidelines (IICARus). Res Integr Peer Rev. 2019;4:12. https://doi.org/10.1186/s41073-019-0069-3 .

Blanco D, Altman D, Moher D, Boutron I, Kirkham JJ, Cobo E. Scoping review on interventions to improve adherence to reporting guidelines in health research. BMJ Open. 2019;9:e026589. https://doi.org/10.1136/bmjopen-2018-026589 .

Charters E. The use of think-aloud methods in qualitative research: an introduction to think-aloud methods. Brock Educ J. 2003;12:68–82. https://doi.org/10.26522/brocked.v12i2.38 .

Article   Google Scholar  

Welch V, Petticrew M, Tugwell P, PRISMA-Equity Bellagio group, et al. PRISMA-equity 2012 extension: reporting guidelines for systematic reviews with a focus on health equity. PLoS Med. 2012;9:e1001333. https://doi.org/10.1371/journal.pmed.1001333 .

Wang X, Chen Y, Liu Y, et al. Reporting items for systematic reviews and meta-analyses of acupuncture: the PRISMA for acupuncture checklist. BMC Complement Altern Med. 2019;19:208. https://doi.org/10.1186/s12906-019-2624-3 .

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Acknowledgements

We dedicate this paper to the late Douglas G Altman and Alessandro Liberati, whose contributions were fundamental to the development and implementation of the original PRISMA statement.

We thank the following contributors who completed the survey to inform discussions at the development meeting: Xavier Armoiry, Edoardo Aromataris, Ana Patricia Ayala, Ethan M Balk, Virginia Barbour, Elaine Beller, Jesse A Berlin, Lisa Bero, Zhao-Xiang Bian, Jean Joel Bigna, Ferrán Catalá-López, Anna Chaimani, Mike Clarke, Tammy Clifford, Ioana A Cristea, Miranda Cumpston, Sofia Dias, Corinna Dressler, Ivan D Florez, Joel J Gagnier, Chantelle Garritty, Long Ge, Davina Ghersi, Sean Grant, Gordon Guyatt, Neal R Haddaway, Julian PT Higgins, Sally Hopewell, Brian Hutton, Jamie J Kirkham, Jos Kleijnen, Julia Koricheva, Joey SW Kwong, Toby J Lasserson, Julia H Littell, Yoon K Loke, Malcolm R Macleod, Chris G Maher, Ana Marušic, Dimitris Mavridis, Jessie McGowan, Matthew DF McInnes, Philippa Middleton, Karel G Moons, Zachary Munn, Jane Noyes, Barbara Nußbaumer-Streit, Donald L Patrick, Tatiana Pereira-Cenci, Ba′ Pham, Bob Phillips, Dawid Pieper, Michelle Pollock, Daniel S Quintana, Drummond Rennie, Melissa L Rethlefsen, Hannah R Rothstein, Maroeska M Rovers, Rebecca Ryan, Georgia Salanti, Ian J Saldanha, Margaret Sampson, Nancy Santesso, Rafael Sarkis-Onofre, Jelena Savović, Christopher H Schmid, Kenneth F Schulz, Guido Schwarzer, Beverley J Shea, Paul G Shekelle, Farhad Shokraneh, Mark Simmonds, Nicole Skoetz, Sharon E Straus, Anneliese Synnot, Emily E Tanner-Smith, Brett D Thombs, Hilary Thomson, Alexander Tsertsvadze, Peter Tugwell, Tari Turner, Lesley Uttley, Jeffrey C Valentine, Matt Vassar, Areti Angeliki Veroniki, Meera Viswanathan, Cole Wayant, Paul Whaley, and Kehu Yang. We thank the following contributors who provided feedback on a preliminary version of the PRISMA 2020 checklist: Jo Abbott, Fionn Büttner, Patricia Correia-Santos, Victoria Freeman, Emily A Hennessy, Rakibul Islam, Amalia (Emily) Karahalios, Kasper Krommes, Andreas Lundh, Dafne Port Nascimento, Davina Robson, Catherine Schenck-Yglesias, Mary M Scott, Sarah Tanveer and Pavel Zhelnov. We thank Abigail H Goben, Melissa L Rethlefsen, Tanja Rombey, Anna Scott, and Farhad Shokraneh for their helpful comments on the preprints of the PRISMA 2020 papers. We thank Edoardo Aromataris, Stephanie Chang, Toby Lasserson and David Schriger for their helpful peer review comments on the PRISMA 2020 papers.

Provenance and peer review

Not commissioned; externally peer reviewed.

Patient and public involvement

Patients and the public were not involved in this methodological research. We plan to disseminate the research widely, including to community participants in evidence synthesis organisations.

There was no direct funding for this research. MJP is supported by an Australian Research Council Discovery Early Career Researcher Award (DE200101618) and was previously supported by an Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (1088535) during the conduct of this research. JEM is supported by an Australian NHMRC Career Development Fellowship (1143429). TCH is supported by an Australian NHMRC Senior Research Fellowship (1154607). JMT is supported by Evidence Partners Inc. JMG is supported by a Tier 1 Canada Research Chair in Health Knowledge Transfer and Uptake. MML is supported by The Ottawa Hospital Anaesthesia Alternate Funds Association and a Faculty of Medicine Junior Research Chair. TL is supported by funding from the National Eye Institute (UG1EY020522), National Institutes of Health, United States. LAM is supported by a National Institute for Health Research Doctoral Research Fellowship (DRF-2018-11-ST2–048). ACT is supported by a Tier 2 Canada Research Chair in Knowledge Synthesis. DM is supported in part by a University Research Chair, University of Ottawa. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

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School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

Matthew J. Page, Joanne E. McKenzie, Sue E. Brennan & Steve McDonald

Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, Netherlands

Patrick M. Bossuyt

Université de Paris, Centre of Epidemiology and Statistics (CRESS), Inserm, F 75004, Paris, France

Isabelle Boutron

Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Gold Coast, Australia

Tammy C. Hoffmann

Annals of Internal Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA

Cynthia D. Mulrow

Knowledge Translation Program, Li Ka Shing Knowledge Institute, Toronto, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada

Larissa Shamseer

Evidence Partners, Ottawa, Canada

Jennifer M. Tetzlaff

Clinical Research Institute, American University of Beirut, Beirut, Lebanon; Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

Elie A. Akl

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA

York Health Economics Consortium (YHEC Ltd), University of York, York, UK

Julie Glanville

Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; Department of Medicine, University of Ottawa, Ottawa, Canada

Jeremy M. Grimshaw

Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, JB Winsløwsvej 9b, 3rd Floor, 5000 Odense, Denmark; Open Patient data Exploratory Network (OPEN), Odense University Hospital, Odense, Denmark

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Department of Anesthesiology and Pain Medicine, The Ottawa Hospital, Ottawa, Canada; Clinical Epidemiology Program, Blueprint Translational Research Group, Ottawa Hospital Research Institute, Ottawa, Canada; Regenerative Medicine Program, Ottawa Hospital Research Institute, Ottawa, Canada

Manoj M. Lalu

Department of Ophthalmology, School of Medicine, University of Colorado Denver, Denver, Colorado, United States; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Tianjing Li

Division of Headache, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA; Head of Research, The BMJ, London, UK

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Evan Mayo-Wilson

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

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Lesley A. Stewart

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James Thomas

Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Unity Health Toronto, Toronto, Canada; Epidemiology Division of the Dalla Lana School of Public Health and the Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada; Queen’s Collaboration for Health Care Quality Joanna Briggs Institute Centre of Excellence, Queen’s University, Kingston, Canada

Andrea C. Tricco

Methods Centre, Bruyère Research Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, Canada

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Contributions

JEM and DM are joint senior authors. MJP, JEM, PMB, IB, TCH, CDM, LS, and DM conceived this paper and designed the literature review and survey conducted to inform the guideline content. MJP conducted the literature review, administered the survey and analysed the data for both. MJP prepared all materials for the development meeting. MJP and JEM presented proposals at the development meeting. All authors except for TCH, JMT, EAA, SEB, and LAM attended the development meeting. MJP and JEM took and consolidated notes from the development meeting. MJP and JEM led the drafting and editing of the article. JEM, PMB, IB, TCH, LS, JMT, EAA, SEB, RC, JG, AH, TL, EMW, SM, LAM, LAS, JT, ACT, PW, and DM drafted particular sections of the article. All authors were involved in revising the article critically for important intellectual content. All authors approved the final version of the article. MJP is the guarantor of this work. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

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Correspondence to Matthew J. Page .

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Competing interests.

All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/conflicts-of-interest/ and declare: EL is head of research for the BMJ ; MJP is an editorial board member for PLOS Medicine ; ACT is an associate editor and MJP, TL, EMW, and DM are editorial board members for the Journal of Clinical Epidemiology ; DM and LAS were editors in chief, LS, JMT, and ACT are associate editors, and JG is an editorial board member for Systematic Reviews . None of these authors were involved in the peer review process or decision to publish. TCH has received personal fees from Elsevier outside the submitted work. EMW has received personal fees from the American Journal for Public Health , for which he is the editor for systematic reviews. VW is editor in chief of the Campbell Collaboration, which produces systematic reviews, and co-convenor of the Campbell and Cochrane equity methods group. DM is chair of the EQUATOR Network, IB is adjunct director of the French EQUATOR Centre and TCH is co-director of the Australasian EQUATOR Centre, which advocates for the use of reporting guidelines to improve the quality of reporting in research articles. JMT received salary from Evidence Partners, creator of DistillerSR software for systematic reviews; Evidence Partners was not involved in the design or outcomes of the statement, and the views expressed solely represent those of the author.

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Supplementary Information

Additional file 1..

PRISMA 2020 checklist.

Additional file 2.

PRISMA 2020 expanded checklist.

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Page, M.J., McKenzie, J.E., Bossuyt, P.M. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev 10 , 89 (2021). https://doi.org/10.1186/s13643-021-01626-4

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About Systematic Reviews

Summary of Findings Table in a Systematic Review

systematic review study characteristics table

Automate every stage of your literature review to produce evidence-based research faster and more accurately.

What is a summary of findings table.

The Cochrane Review defines the “summary of findings table” as a structured tabular format in which the primary findings of a review, particularly information related to the quality of evidence, the magnitude of the effects of the studied interventions, and the aggregate of available data on the main outcomes, are presented. It includes multiple pieces of data derived from both quantitative and qualitative data analysis in systematic reviews . These include information about the main outcomes, the type and number of studies included, the estimates (both relative and absolute) of the effect or association, and important comments about the review, all written in a plain-language summary so that it’s easily interpreted. It also includes a grade of the quality of evidence; i.e., a rating of its certainty.

Most systematic reviews are expected to have one summary of findings table. But some studies may have multiple, if the review addresses more than one comparison, or deals with substantially different populations that require separate tables. The studies in a table can also be grouped in terms of applied intervention type, type of outcome measure, the type of participants, the study design etc..

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systematic review study characteristics table

What Does A Summary Of Findings Table Include?

A summary of findings table typically includes the following information:

  • A description of the population and setting addressed by the available evidence
  • A description of comparisons addressed in the table, including all interventions
  • A list of the most important outcomes, whether desirable or undesirable (limited to seven)
  • A measure of the burdens of each outcome
  • The magnitude of effect measured for each outcome (both absolute and relative)
  • The participants and studies analyzed for each outcome
  • An assessment of the certainty of the evidence for each outcome (typically using GRADE)
  • Explanations

It’s best to include evidence profiles, i.e. additional tables that support the data in the summary of findings, to which the review may be linked. It also may be neat to have a study descriptor table different from a results table. The study descriptor table shows information about the characteristics of included studies, like study design, study region, participant information, etc. The results table mostly contains outcomes, outcome measures, study results, etc. These can help provide readers with more context about the review, and its conclusions.

Final Takeaway

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systematic review study characteristics table

Systematic Reviews in Health

  • Introduction
  • Research Question
  • Research Protocol
  • Database Searching
  • Article Screening

Data Extraction

Study characteristics table.

  • Quality Appraisal
  • Evidence Synthesis
  • Interpret Results
  • Reporting with PRISMA 2020

Once the search and selection of studies for inclusion is completed the next step is to read the full text of each article identified for inclusion in the Systematic Review and extract the relevant data using a standardised data extraction form.   The purpose of the data extraction step is to:

  • Objectively and accurately summarize studies in a common format to facilitate synthesis,
  • Identify numerical data if a meta-analysis is to take place, and
  • Obtain information to objectively assess the risk of bias in, and applicability of, studies.

The standardised data extraction form is as long or as short as necessary and can be coded if desired, especially if a quantitative analysis is required.  If the Systematic Review is a narrative (no meta-analysis) and/or reviews a relatively small number of studies, coding is probably unnecessary.  

Ideally, at least two reviewers should work independently, to extract quantitative and other critical data from each study, and a fair procedure for resolving discrepancies should be established.

Data Extraction Tools

Tools available for data extraction include an Excel spreadsheet or the data extraction templates in software like Covidence.   If the Systematic Review includes a meta-analysis and/or reviews a large number of studies the data extraction software will likely be helpful.  Using the excel spreadsheet allows for more customisation of what data to collect.

What Data to Collect?

Reviewers should develop the standardised form to suit the specific Systematic Review 1 and use the key question(s) and inclusion and exclusion criteria as a guide.   Use the PICOT framework to choose data elements in the data extraction form.  Anticipate what the data summary table will need to include.

The following is an example of elements to include in a standardised data extraction form.

Completed data extraction forms can be used to produce a summary table of study characteristics that were considered important for inclusion in the Systematic Review.  

The completed summary table should be included in the Results section of the Report of the Systematic Review, either as an appendix or the in the body of the text.

  • Example study characteristics table.2

1. Institute of Medicine of the National Acadamies. (2011). Finding what works in health care: Standards for systematic reviews. Available: http://iom.nationalacademies.org/Reports/2011/Finding-What-Works-in-Health-Care-Standards-for-Systematic-Reviews/Standards.aspx

2. Hathorn, E., et al. (2014). The effectiveness of gentamicin in the treatment of Neisseria gonorrhoeae: A systematic review. Systematic Reviews, 3, 1, 104. Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4188483/

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Systematic Reviews: Study selection and appraisal

  • Types of literature review, methods, & resources
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  • Medical Literature Databases to search
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Study selection: PRISMA Item 9

Inclusion/Exclusion criteria

See  http://unimelb.libguides.com/c.php?g=492361&p=3368110

First level screening - title and abstract review

At the initial screening stage read just the title and abstract of the candidate studies and make a decision to include or exclude the study from your review.  

For small reviews of a few studies (e.g. <100)

T he research team should agree on the inclusion and exclusion criteria for studies you wish to review and  put together a study screening form.   To help identify  your inclusion/exclusion criteria, r evisit the  PICOS  of interest  you came up with for your  search strategy  and gain agreement/approval from your colleagues or supervisor . The  screening form may look similar to  Table 3  of  Brown et al (2013) . You may write down your decision to include or exclude an article on an Excel spreadsheet  like this one , or if you have a small number of records you may choose to print out one copy for each record, although printing will be impractical for larger numbers of records .  S creen each potentially useful article identified in the literature search as follows:

  • Read the title and abstract (where available) and apply the inclusion/exclusion criteria from the screening form.
  • Make a decision on whether or not to include the study in the review.
  • Record the decision and reasons for inclusion/exclusion on the study screening form or spreadsheet . You will summarise the reasons for exclusion on the PRISMA flow diagram - see Study Selection PRISMA item # 17 below.

For large reviews of many studies (e.g. >100) - in case you need to partially automate the screening process

There are three web-based software applications that can help with screening and tracking your selection decisions:

  • Covidence  (GW in 2019 bought a subscription so you can use this tool now). Provides a decision dashboard and annotation tool, and the ability to screen candidate citations you locate in your literature search. Covidence is used by Cochrane review teams as their first level screening tool, the resulting study characteristics and decision data can be exported to  RevMan  (free for academic use) or Excel. 
  • Abstrackr  (free, Beta, open-source). Abstrackr comprises two components; a web-based annotation tool that allows participants in a review to collaboratively screen citations for relevance, and machine learning technologies that semi-automate the screening process. The web-based annotation tool allows project leads to import the citations that are to be screened for a review from either RefMan or Pubmed. Participants can then join the project and begin screening; the tool maintains a digital paper trail of all screening decisions.  The machine learning technology permits reviewers to screen roughly half of the set of citations imported for a given review, and then let the software automatically exclude a (hopefully large) portion of the remaining citations; the reviewers will then only need to screen the articles classified as relevant by the software.  A recent article evaluating the use of Abstrackr in the systematic review process is  Rathbone, J., Hoffmann, T., & Glasziou, P. (2015). Faster title and abstract screening? Evaluating Abstrackr, a semi-automated online screening program for systematic reviewers. Systematic Reviews, 480. doi:10.1186/s13643-015-0067-6
  • DistillerSR  (requires subscription). Enables you to create forms for making screening decisions, and extract data.

Second level screening - full text review

Having excluded candidate studies that did not meet your inclusion/exclusion criteria you should have a smaller number of potentially relevant studies. GW affiliates at GW and Children's National Health System can use Box to store and share the full text PDF's of copyrighted journal articles https://it.gwu.edu/backup-storage-document-management . Read and critically appraise  the full text of each study you selected at the first pass screening stage to determine whether you wish to include them in your discussion and analysis. Specifically each study must be evaluated based on the following criteria:

Does this study address a clearly focused question? Did the study use valid methods to address this question? Are the valid results of this study important? Are these valid, important results applicable to my patient or population?

If the answer to any of these questions is “no”, you may wish to read no further and exclude the study, or you may decide to include the study to inform your discussion but not include the results in your analysis. 

To help with this process you may wish to download and apply one of the following Critical Appraisal tools:

Study Quality Assessment Tools developed in 2013 by the National Heart Lung & Blood Institute: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools   Choose an appraisal tool that matches the type of study you are reviewing from one of the following 6 study types: Controlled Intervention Studies, Systematic Reviews and Meta-Analyses, Observational Cohort and Cross-Sectional Studies, Case-Control Studies, Before-After (Pre-Post) Studies With No Control Group, & Case Series Studies.

Worksheets from the Oxford University Center for Evidence Based Medicine - choose a worksheet that matches the type of study: Systematic Review article Critical Appraisal Sheet Diagnosis study Critical Appraisal Sheet Prognosis study Critical Appraisal Sheet Therapy / Randomized Controlled Trial Critical Appraisal Sheet

Alternatively the  CASP: Critical Appraisal Skills Checklists  are eight critical appraisal tools designed to be used when reading and evaluating the quality of Systematic Reviews, Randomised Controlled Trials, Cohort Studies, Case Control Studies, Economic Evaluations, Diagnostic Studies, Qualitative studies and Clinical Prediction Rule.

Another alternative set of Critical Appraisal checklists  are from the Joanna Briggs Institute (JBI). JBI require you use their critical appraisal checklists if you are conducing a JBI systematic review following the methods described in the JBI Manual for Evidence Synthesis .

Make a decision on whether or not to include the study in your review, and write your decision and reasons for inclusion/exclusion at this second level/full text review stage on the study screening form. You will summarize the reasons for exclusion on the PRISMA flow diagram - see Study Selection PRISMA item # 17 below.

Reporting your screening decisions

In the final report in the methods section the PRISMA checklist Item 9 study selection will be reported as:

  • How studies were screened e.g. by reading title & abstract, and how they were critically appraised e.g. by applying a standardised appraisal form appropriate for that study type - see above.
  • What sort of studies were excluded e.g. letters, conference abstracts, etc.
  • Who reviewed/appraised the studies
  • What the process was for resolving disagreements e.g. reporting the level of inter-rater agreement, how often arbitration about selection was required, & what efforts were made to resolve disagreement e.g. were original authors contacted

Study selection PRISMA Item 17

Researchers must keep the screening forms to create a summary descriptive flow diagram of study selection.

In the final report in the results section the PRISMA checklist Item 17 study selection should be reported as follows:

  • Record the number of studies screened, assessed for eligibility and included in the review, with reasons for exclusions both in the text and in form of a PRISMA flow diagram of study selection e.g. similar to Fig 2 of Liberati et al. (2009). Covidence keeps track of your screening decisions and generates a PRISMA flow diagram for you, GW affiliates can register for a Covidence account here . Alternatively there is a PRISMA flow diagram generator at  http://www.prisma-statement.org/PRISMAStatement/FlowDiagram
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Cardiac troponins and coronary artery calcium score: a systematic review

  • Naghmeh Shahraki 1   na1 ,
  • Sara Samadi 2   na1 ,
  • Omid Arasteh 1 ,
  • Reza Javidi Dashtbayaz 3 ,
  • Batool Zarei 1 ,
  • Amir Hooshang Mohammadpour 1 , 4 &
  • Vahid Jomehzadeh   ORCID: orcid.org/0000-0003-3300-2777 5  

BMC Cardiovascular Disorders volume  24 , Article number:  96 ( 2024 ) Cite this article

168 Accesses

Metrics details

An early diagnosis of atherosclerosis, particularly in subclinical status, can play a remarkable role in reducing mortality and morbidity. Because of coronary artery calcification (CAC) nature in radiation exposure, finding biomarkers associated with CAC could be useful in identifying individuals at high risk of CAC score. In this review, we focused on the association of cardiac troponins (hs-cTns) and CAC to achieve insight into the pathophysiology of CAC. In October 2022, we systematically searched Web of Science, Scopus, PubMed, and Embase databases to find human observational studies which have investigated the association of CAC with cardiac troponins. To appraise the included articles, we used the Newcastle Ottawa scale (NOS). Out of 520 records, 10 eligible studies were included. Based on findings from longitudinal studies and cross-sectional analyses, troponin T and I were correlated with occurrence of CAC and its severity. Two of the most important risk factors that affect the correlation between hs-cTns serum levels and CAC were age and gender. The elevation of cardiac troponins may affect the progression of CAC and future cardiovascular diseases. Verifying the association between cardiac troponins and CAC may lead to identify individuals exposed to enhanced risk of cardiovascular disease (CVD) complications and could establish innovative targets for pharmacological therapy.

Peer Review reports

Introduction

Coronary artery calcium (CAC) is known to be associated closely with atherosclerotic plaque, and predicts the incident of cardiovascular events and mortality [ 1 , 2 , 3 ]. It is estimated that by 2035, almost one-half of the population will have cardiovascular diseases, with projected costs of over one trillion dollars [ 4 ]. Among the many potentially helpful options, CAC evaluating plays an important role as a risk stratification tool with guideline endorsement for shared decision making in asymptomatic individuals aged 40–75 years, free of atherosclerotic cardiovascular disease (ASCVD) [ 5 , 6 ]. Moreover, scanning coronary computed tomography (CCT) is capable of reclassifying patients with an intermediate risk for coronary artery disease (CAD), quantifying the specks of calcium within atherosclerotic lesions [ 7 , 8 ]. By a multi-ethnic cohort of individuals without known CAD with a follow-up of 3.8 years, Detrano et al. demonstrated that the agatson score, reflecting the total area of calcium deposits, is a strong predictor of incident coronary heart disease [ 9 ]. Calcium scores under 100 are unlikely to be associated with severe stenosis on coronary angiography and represent a very low risk for obstructive CAD [ 10 , 11 ]. Nowadays, risk assessment is an important part of routine clinical practice and tools for prediction of CAD events in healthy subjects and the correlated administration of preventive cures have a long history [ 12 ]. Cardiac troponins (hs-cTn T and I) are highly sensitive and specific biomarkers which have been shown to be predictive of poorer long-term cardiovascular outcomes in stable patients [ 13 ]. These cardiac regulatory proteins control the calcium mediated interaction between actin and myosin. It has been extensively demonstrated that troponin levels play a pivotal role in development of cardiovascular disease, including coronary heart diseases (CHD) [ 14 ]. High-sensitivity cardiac troponin (hs-cTn) I and T assays quantify cTn in most healthy men and women and facilitate risk stratification for cardiovascular disease in both acute and outpatient settings [ 15 , 16 , 17 ]. With the development of hs-cTn assays, not only CAD but also subclinical CAC can be diagnosed [ 18 ]. However, European guidelines still do not recommend general use of cardiac troponins as a risk biomarker [ 19 ]. So far, various studies have been conducted to investigate the relationship between hs-cTn serum levels and the CAC diagnosis. Some studies have demonstrated that increased hs-cTn levels in plasma are strongly correlated with CAC risk increasing and CVD [ 20 ]. As illustrated in such studies, elevated troponin T levels showed a greater rate of arterial calcification risk [ 14 , 21 ]. A study that mentioned it in detail demonstrated that an increase above 3 ng/l in hs-cTn T serum level was associated with elevated risk of CAC. However, several investigations failed to indicate the relationship between high hs-cTn T plasma concentration and enhanced risk of CAC. In case of troponin I level, a study was performed on a group of athletes showed that increasing in hs-cTn I plasma level could help to recognize CAC development and further CAD risk stratifications. Moreover, several studies mentioned that an increase in serum troponin I levels was directly related to an increase in agatson score [ 21 , 22 ]. In this systematic review, we have gathered and overviewed articles that examined the association between serum troponin T and I levels and coronary artery calcium score to see if serum troponins could be considered as reliable factors for diagnosing CAC.

Protocol and registration

The current study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement and was registered in the PROSPERO database (CRD42021246161).

Search strategy

We searched Pubmed, Web of sciences, Scopus, and Embase with no language and time restrictions to find eligible articles. The keywords used as search bases were obtained from Mesh terms, Emtree terms, and hand searching. The Mesh terms and keywords were obtained from PubMed and Emtree. Our search was conducted with the Mesh terms of cardiovascular diseases, coronary artery disease, coronary disease, troponin, troponin T, and troponin I.

Inclusion criteria

In the first step, two researchers independently skimmed the articles based on their titles and abstracts. Animal studies, in-vitro experiments, review articles, case reports, clinical trials, editorials, and clinical guidelines were excluded. The conference articles were also excluded due to the lack of required full texts. The studies that did not present a way of comparison were excluded. The only acceptable comorbidities in patients were CVD, type 2 diabetes, metabolic syndrome, and hypertension. As a result, studies that included patients with other comorbidities were excluded to decrease the risk of bias. Furthermore, full texts of the related papers were studied carefully by the same two researchers to see if they were compatible with the inclusion criteria or not. Any disagreements between the two authors were resolved with careful discussion of the third researcher. The inclusion criteria were defined based on the PECO template; population was coronary artery disease and asymptomatic individuals, exposure was cardiac troponins, characterized by elevation of cardiac troponins including troponin I and troponin T, and the outcome was CAC. We defined this template to systematically investigate the observational studies that mentioned the relationship between cardiac troponins and CAC scores.

Data extraction and quality assessment

Two researchers independently performed data extraction and the following information was extracted from the included studies by the two reviewers: author’s name, year, country of the study population, age, study design, follow-up duration (for cohort studies), study population and number of participants, effect sizes and risk estimates (Odds ratios; OR) with their confidence intervals (CI), and covariates in the multivariable model. Included studies were appraised using Newcastle Ottawa scale (NOS) for observational studies; cohort, case control, and cross-sectional studies. Based on the NOS scale, a score of ≥ 7 is considered good quality. Because of significant heterogeneity among the articles, whether in study design or various cardiac troponins, a meta-analysis on the presented data was failed to conduct.

Results of the literature search

After the screening process, 27 articles seemed potentially eligible based on their titles and abstracts. Three studies used various therapeutic options or electroconvulsive therapy for their patients; therefore, were excluded due to the great risk of bias. Non-English articles were also included except for one Chinese paper, which had a published English duplicate with the same results and more complete data; hence we included the English version. Finally, 10 articles that matched our PECO template were identified for inclusion [ 17 , 18 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. The complete flowchart of the study selection method is provided as Fig.  1 .

figure 1

Flowchart of the study

General characteristics of included studies

The majority of the studies in the current review were cross-sectional ( n  = 8) and two were cohort in design [ 25 ]. The mean age of participants in the studies ranged from 42 to 76 years. Also, in most of the studies, the male/female ratio in the population of selected participants has been considered, which indicates that the conclusions could be generalized to the community. The effect size of studies varied from 76 patients [ 21 ] to 1844 participants [ 22 ]. These observational studies were conducted in various continents, but the majority of them were carried out in Europe. So, the results have covered different populations. In all articles, high-sensitivity devices have been used to measure serum hs-cTn levels and serum concentration of hs-cTn I ranged from 1.5 to 32 ng/l and hs-cTn T serum concentration was between 3.46 and 17.9 ng/l. The baseline body mass index (BMI) of the participants indicated that most of the studies examined overweight and obese subjects (BMI > 24 kg/m2). Other studies recruited individuals with mean BMI of less than 30 kg/m2. According to the quality assessments, most of the included studies were classified as good studies.

Association of cardiac proteins with CAC

The main characteristics of the studies included in the systematic review are summarized in Tables  1 and 2 .

Troponin T and CAC association

All of the studies that have examined the relationship between hs-cTn T and CAC score concluded that the more serum hs-cTn T concentration increased, the more amount of agatson score rose. In one cross-sectional study that assess the relationship between hs-cTn T and CAC score, 229 male patients with stable angina and unknown CAD were studied. In these 60 years old patients, higher CAC scores were seen in patients with significantly elevated levels of hs-Tn T ( P  < 0.005). Also, in a multivariate model, CAC score was an independent predictor of the plasma hs-cTn T (coefficient = 0.06, SE = 0.02; P  = 0.0089). Overall, this study concluded that the presence and extent of coronary atherosclerosis is associated noticeably with the circulating levels of hs-cTn T [ 23 ]. In another cross-sectional study, 215 consecutive, stable patients with clinical suspicion of coronary artery disease were enrolled. It is demonstrated a clear significant association between serum hs-cTn T (LoD: 3 ng/L) concentrations and subclinical atherosclerosis degree as determined by coronary calcium and expressed through the agatson score. One of the limitations of this study was that the participants enrolled were 69 years old Japanese men and women. Therefore, these findings may not be generalized to other ethnic groups. Also, more participants are needed to warrant the study results [ 24 ]. In a study by Alexander C. Razavi et al., 574 patients with D2TM ( n  = 152) or metabolic syndrome ( n  = 422) at baseline were selected from the MESA cohort and their CAC levels were prospectively evaluated. Two third of the study population were women and the average age was 58.9 years old. It was clear that the participants who had the long-term absence of CAC were younger and they had lower fasting blood glucose and hs-cTn T level. In addition, those with the CAC score of zero did not have a carotid artery plaque. Also, 55% higher odds of long-term absence of CAC was observed in patients with serum hs-cTn T concentration < 3 mg/dl as compared with those with hs-cTn T ≥ 3 mg/dl ( p  = 0.04). The results of this study showed that hs-cTn T level elevation may reflect both subclinical myocardial injury and systemic arterial stiffness in persons with metabolic disease. As a result of this study, an increase in hs-cTn T levels and severity of metabolic syndrome was considered as potential ASCVD risk factors which could predict the arterial aging and CAC [ 25 ].

Lazzarino et al. recruited 430 participants drawn from the Whitehall II epidemiological cohort and aged 53–76 years with no history of clinical or subclinical CVD and no previous diagnosis or treatment for hypertension, inflammatory diseases, allergies, or kidney disease to evaluate the effectiveness of the Framingham, Joint British Societies & British National Formulary (JBS/BNF), Assign, and Q-Risk 2 scores in identifying subjects with detectable hs-CTn T in circulation. They also determined whether the scores’ estimates are influenced by CAC and to what extent. Their founding illustrated that if the mentioned risk algorithms are arranged based on the ROC areas, the age and gender model has the highest ranking, followed by Q-Risk2, Framingham, JBS/BNF, and Assign. Nevertheless, when the scores are arranged regarding the degree of mediation by CAC, an essentially reversed order could be seen. This implies that as the accuracy of a score in predicting detectable hs-CTn T increases, its dependence on CAC as a mediator decreases. Alternatively, a score that effectively identifies atherosclerosis has a reduced ability to predict cardiac damage ( P  = 0.009) [ 27 ]. Study by Sandoval et al. has examined the relationship between hs-cTn T and CAC severity in 6,749 participants free of clinical cardiovascular disease at baseline during 15 years. In this study, it was identified that participants with detectable CAC had a higher incidence rate of CVD than those with undetectable CAC. Also, individuals with traceable hs-cTn T (> 3 ng/l) had a higher CAC level. Moreover, it was shown that hs-cTn T was an independent risk factor for CVD incidence in multivariable Cox regression analyses. In the adjusted analysis models, it was found that the relationship between detectable hs-cTn T and CVD is significant mostly in women not in men (HR: 1.7 vs. 1.49) [ 17 ]. These results extend the value of hs-cTn T, which is a prognostic factor for short and long term CVD outcomes.3.3.2. Studies measured both troponins (T and I) and CAC.

A cross-sectional study on 76 consecutive patients undergoing CCT during routine clinical care was done prospectively to measure the cardiac biomarkers, hs-cTn T and hs-cTn I concentrations (LoD: 0.005 µg/l and LoD: 1.1–1.9 ng/l respectively) in association with CAC. In other words, in both univariate and multivariate logistic regression models, hs-cTn biomarkers were significantly correlated with increased agatson scores. One of the limitations of this study was the small sample of patients that could not be proposed for the general population. Moreover, the people who were selected from the PROMISE trial were mostly Caucasian individuals that did not reflect multiethnic cohorts [ 21 ]. In another study, 706 patients with 65 years old age who suspected chronic coronary syndrome (CCS) and were undergone for angiographic evaluation of CAD checkup were examined. It was depicted that both hs-cTn concentrations were significantly higher in CAD50 patients than in non-obstructive CAD and the ones without CAD ( p  < 0.001). Although the higher concentrations of hs-cTn I and T were related to CAD50 in unadjusted analysis (OR 1.45, 95% CI [1.28–1.64], p  < 0.001, hs-cTn T: OR 1.27 [1.13–1.41], p  < 0.001), it was mentioned that just hs-cTn I concentration was significantly associated with CAD50 after adjustment for age, sex, smoking, history of CAD, diabetes and HF, BMI, SBP, LDL-C and eGFR (OR 1.20 [1.05–1.38], p  = 0.009) [ 26 ]. Another cross sectional study on 1864 individuals with chest pain discomfort was performed by Cardinaels et al. which evaluated the hs-cTn T and hs-cTn I concentrations in relation with CAC. The average age of these patients was about 55.8 ± 11.0 and the ratio of men to women was 56.0%. It was shown that hs-cTn concentrations were remarkably associated with the coronary calcium score according to both univariate and multivariate linear regression analysis ( P  < 0.001) [ 18 ].

Troponin I and CAC association

A study by James L. Januzzi et al. conducted on 1844 stable symptomatic outpatient and revealed that hs-Tn I level was associated with the transition from non-calcified to calcified vascular plaque. The authors adjusted correlations for differences in age, and gender. It is suggested that higher circulating hs-Tn I levels were more related to the CAD progression prospectively with no dependence on other patient characteristics. Moreover, higher hsTn I concentrations were a predictive factor for moderate and severe coronary obstruction. CAC scores exhibited weak bivariate correlation with log hsTn I when added to multivariable linear regression models [ 22 ]. Moreover, the relationship between hs-Tn I and CAC was evaluated by Olson et al. using logistic regression analyses and receiver operating characteristic curves (ROC). This investigation was performed on 1173 randomized, middle-aged subjects without known CVD, indicating 29% presence of CAC (agatson score > 0) in the lowest quartile of hs-Tn I compared to 55% at the highest rate, with a step-by-step increase over quarters. The Spearman correlation coefficient between hs-Tn I and CAC was 0.23, which showed the strong correlation between these two factors ( p  < 0.0001) [ 28 ].

Troponins (T and I) and CAC risk in asymptomatic individuals

In a population-based cross-sectional study with normally gender distributed patients aged 58 years old or above, participants undergoing coronary computed tomography (CCT) as part of their routine clinical care were consecutively included. According to the results, in these cardiovascular asymptomatic patients, the more CAC was measured the more concentration of both hs-Tn T and I was reported both in univariable and multivariable linear regression models. Individuals who had high levels of hs-cTnT (≥ 0.02 µg/l) and hs-cTnI (≥ 5.5 ng/l) were more prone to displaying CAC values ≥ 400 [ 21 ].

Another study by Lazzarino et al., recruiting disease-free subjects suggests that as the accuracy of a score in predicting detectable hs-CTnT increases, its reliance on CAC as a mediator decreases. In other words, a score that effectively identifies atherosclerosis has a diminished ability to predict cardiac damage. A limitation of this study is it’s cross-sectional nature, in which the evaluation of hs-cTn T was not considered in a prospective manner and it was not exempt from selection bias [ 27 ]. In a prospective cohort study of Multi-Ethnic Study of Atherosclerosis (MESA) with median follow-up of 15 years, 1,002 ASCVD incidents occurred among 6,749 individuals free of clinical CVD with a mean age of 62 (10) years and 53% women. It was shown that subjects with detectable hs-cTnT (HR, 1.47; 95% CI, 1.21–1.77; p 0.001) and detectable CAC (HR, 2.35; 95% confidence interval [CI], 2.0 -2.76; p 0.001) possessed increased rates of ASCVD compared with undetectable findings. Similarly, participants with undetectable hs-cTnT (32%) and subjects with zero CAC (50%) both showed comparable risks for ASCVD. Therefore, utilizing both markers together enhances the accuracy of risk prediction [ 17 ]. Additionally, in a cross-sectional study, 1173 asymptomatic participants were chosen at random from the Danish community; 52% of them were female and between the ages of 50 and 60. Logistic regression analyses were used to determine the distribution of the agatson score and hs-TnI quartiles throughout the entire population. Results showed that the differences in hs-TnI and CAC between men and women were statistically significant ( p  < 0.0001). When employing hs-TnI quartiles as a predictor, univariate regression revealed that for all dichotomous CAC outcomes, being in a higher hs-TnI quartile carried a stepwise increased chance of having a greater CAC burden. When adjusting for cardiovascular risk factors, being in the highest hs-TnI quartile led to a 56% increased risk of having an agatson score > 0 and a 82% enhanced risk of having an agatson score > 100 when compared to the lowest quartile. However, Hs-TnI was not able to predict an agatson score > 400. An increase of 1 in the log-transformed hs-TnI led to a 27% accelerated risk for falling into a higher CAC category after adjustment for risk factors [ 28 ].

As CAC measurement is rather expensive and implies radiation exposure, this study aims to describe clinical evidence in case of examining the prognostic role of cardiac troponins in determining the risk of CAC. We systematically reviewed ten cross-sectional ( n  = 6545) and two cohort ( n  = 7323) studies regarding association of cardiac troponins and CAC. Variability of the results between included studies might be the result of difference in methodological design and patient characteristics. Despite variables such as population sample sizes, age, inclusion criteria, primary inflammatory markers studied, and analysis, several studies reported a significant correlation between level of plasma troponin and CAC existence or severity.

The pathophysiological mechanism behind artery calcification has remained unresolved and so the role of various biomarkers such as troponin plasma levels in the process is yet difficult to identify. Coronary artery calcification may occur in different situations and the involved signaling pathways are variously changed in different clinical status. Generally, there are several mechanisms proposed to explain vascular calcification including induction of bone formation, circulating nucleational complexes, and cell death [ 29 ]. Analyses from Cox regression models in a large cohort study by Sandoval et al. with 15-year follow-up and 6,749 participants without cardiovascular disease at baseline has identified that individuals with higher levels of hs-cTn T were subjected to 15.4 events of CVD incidence against 5.2 events for lower hs-cTn T concentrations per 1,000 person-years [ 17 ]. These results highlight the value of detectable/undetectable CAC/hs-cTn T evaluation as a robust prognostic factor for short and long term ASCVD outcomes (20% vs. < 3%). The most important advantages of this study were the long length of observation and evaluation in the multi-ethnic community. In a prospective study by Razavi et al., the healthy arterial aging in individuals with a background of metabolic syndrome or diabetes mellitus was evaluated for 10-years follow-up and the rate of CAC score changes was measured. It was concluded that although the absence of cardiovascular risk factors does not play a role in the rate of CAC progression, the level of hs-cTn T could be a good factor in predicting artery calcification [ 25 ].

Furthermore, there are several cross-sectional studies that showed the association of hsTn T with the incidence and the progression of CAC score was significantly remarkable [ 18 , 21 , 24 ]. On the other hand, a study by Paana et al. showed a lack of correlation between hsTn T and incidence of CAC. However, the small number of participants and their selection from the marathon runners did not represent the whole community in this study which was an important limitation factor [ 30 ]. In order to examine the relationship between hsTn I and CAC severity, Cardinaels et al. represented that hs-cTn I concentrations are significantly correlated with the incidence of CAC [ 18 ]. Also, a study that was done by Januzzi et al. on 1844 stable symptomatic outpatients without known CAD concluded that in case of high concentrations of hsTn I, more prevalent and more extensive obstructive CAD was observed with higher CAC scores [ 22 ]. To better understand this relationship, the study should be performed on a more diverse and larger population.

Some potential reasons for discrepancies in articles’ results were explained by study design and methodological issues, variability in population characteristics and ethnicity, sample size, gender, and method of measuring. Although most studies have used high sensitive methods to measure serum troponin levels, it is difficult to assess low serum troponin concentrations in asymptomatic individuals, and this may be a reason for differences in results. According to the results of the studies, two of the most important risk factors that affect the correlation between hs-cTns serum levels and CAC were age and gender. As Lazzarino et al. mentioned, CAC mediated 6.8% of the impact of age and gender on hs-CTn T in participants without CAC at baseline [ 27 ]. Moreover, in Rusnak et al. study, it has been shown that in different agatson categories, the average age as well as the levels of uric acid are increasing according to rising agatson values which indicated CAC is in the relation with age [ 21 ]. The effect of age is also mentioned in the Kitagawa et al. study in relation with hs-cTn T serum level and both agatson score > 10 and > 400. However, multiple regression analyses demonstrated that serum hs-cTn T increased the odds of both agatson score > 10 and 400 [ 24 ]. Additionally, Cardinaels et al. stated that age is considered as an independent predictor for 30% and 19% of hs-cTn T and hs-cTn I variation respectively [ 18 ]. It is specified in this article that only age, smoking and total cholesterol were significantly different in the event versus non-event group. This is in accordance with the study by Paana et al., which mentioned the significant correlation of age and hs-cTn T concentrations after a run race among athletes [ 30 ]. Ethnicity, also, may play a role in the conflicting results of the studies in this review. It is identified that a weak association between hs-cTn I and CAC was found among studies in which the majority were white population [ 30 ]. Although measurements of serum hs-cTn levels have immense promise as predictive markers for future CHD [ 31 ], currently, there is a lack of strong evidence that they add significantly to global risk assessment. To achieve more precise results, high-qualified prospective studies with matched designs are required to minimize the risk of bias. Nevertheless, this study has some limitations. The included studies were observational, which increases the possibility of bias. Moreover, the design of the studies had some differences which can affect the results. Further high-quality longitudinal studies with larger populations are required to prove these findings. In addition, for future clinical studies, researchers should consider the presence of confounding variables and adjust their study designs to get more accurate results.

Conclusions

The increase of cardiac troponins level may enhance the risk of coronary calcification and future cardiovascular outcomes. Verifying the association between cardiac troponins and CAC may assist to identify individuals susceptible to enhanced risk of CVD complications and could establish innovative targets for pharmacological therapy.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Radford NB, et al. Progression of CAC score and risk of incident CVD. JACC Cardiovasc Imaging. 2016;9(12):1420–9.

Article   PubMed   Google Scholar  

Ilangkovan N, et al. Prevalence of coronary artery calcification in a non-specific chest pain population in emergency and cardiology departments compared with the background population: a prospective cohort study in Southern Denmark with 12-month follow-up of cardiac endpoints. BMJ Open. 2018;8(3):e018391.

Article   PubMed   PubMed Central   Google Scholar  

Bellia C, et al. Fetuin-A is Associated to serum calcium and AHSG T256S genotype but not to coronary artery calcification. Biochem Genet. 2016;54(3):222–31.

Article   CAS   PubMed   Google Scholar  

Benjamin EJ, et al. Heart Disease and Stroke Statistics-2019 update: a Report from the American Heart Association. Circulation. 2019;139(10):e56–28.

Shaikh K, et al. Coronary artery calcification and ethnicity. J Cardiovasc Comput Tomogr. 2019;13(6):353–9.

Greenland P, et al. Coronary calcium score and Cardiovascular Risk. J Am Coll Cardiol. 2018;72(4):434–47.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Budoff MJ, et al. Assessment of coronary artery disease by cardiac computed tomography: a scientific statement from the American Heart Association Committee on Cardiovascular Imaging and Intervention, Council on Cardiovascular Radiology and Intervention, and Committee on Cardiac Imaging, Council on Clinical Cardiology. Circulation. 2006;114(16):1761–91.

Thomas DM, et al. Management of coronary artery calcium and coronary CTA findings. Curr Cardiovasc Imaging Rep. 2015;8(6):18.

Detrano R, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358(13):1336–45.

Knez A, et al. Relation of coronary calcium scores by electron beam tomography to obstructive disease in 2,115 symptomatic patients. Am J Cardiol. 2004;93(9):1150–2.

Mohammadpour AH, et al. Evaluation of RANKL/OPG Serum Concentration Ratio as a New Biomarker for coronary artery calcification: a pilot study. Thrombosis. 2012;2012:306263.

Beatty AL, et al. High-sensitivity cardiac troponin T levels and secondary events in outpatients with coronary heart disease from the Heart and Soul Study. JAMA Intern Med. 2013;173(9):763–9.

Latini R, et al. Prognostic value of very low plasma concentrations of troponin T in patients with stable chronic heart failure. Circulation. 2007;116(11):1242–9.

Willeit P, et al. High-sensitivity Cardiac Troponin concentration and risk of first-ever Cardiovascular outcomes in 154,052 participants. J Am Coll Cardiol. 2017;70(5):558–68.

Chapman AR, et al. Association of High-Sensitivity Cardiac Troponin I Concentration with Cardiac outcomes in patients with suspected Acute Coronary Syndrome. JAMA. 2017;318(19):1913–24.

Sandoval Y, et al. Myocardial infarction risk stratification with a single measurement of high-sensitivity troponin I. J Am Coll Cardiol. 2019;74(3):271–82.

Sandoval Y, et al. Atherosclerotic Cardiovascular Disease Risk Stratification based on measurements of troponin and coronary artery calcium. J Am Coll Cardiol. 2020;76(4):357–70.

Cardinaels EP, et al. High-sensitivity cardiac troponin concentrations in patients with chest discomfort: is it the heart or the kidneys as well? PLoS ONE. 2016;11(4):e0153300.

Piepoli MF, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice: the Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J. 2016;37(29):p2315–2381.

Article   Google Scholar  

Muscente F, De Caterina R. New insights from the MESA study: increased high-sensitivity troponins as a cardiovascular risk factor. Eur Heart J Suppl. 2021;23(Suppl E):E68–e72.

Rusnak J, et al. Comparative analysis of high-sensitivity cardiac troponin I and T for their association with coronary computed tomography-assessed calcium scoring represented by the Agatston score. Eur J Med Res. 2017;22(1):47.

Januzzi JL Jr., et al. High-sensitivity troponin I and Coronary computed tomography in symptomatic outpatients with suspected CAD: insights from the PROMISE trial. JACC Cardiovasc Imaging. 2019;12(6):1047–55.

Caselli C, et al. Effect of coronary atherosclerosis and myocardial ischemia on plasma levels of high-sensitivity troponin T and NT-proBNP in patients with stable angina. Arterioscler Thromb Vasc Biol. 2016;36(4):757–64.

Kitagawa N, et al. High-sensitivity cardiac troponin T is associated with coronary artery calcification. J Cardiovasc Comput Tomogr. 2015;9(3):209–14.

Razavi AC, et al. Predicting Long-Term absence of coronary artery calcium in metabolic syndrome and diabetes. Jacc-Cardiovascular Imaging. 2021;14(1):219–29.

Tveit SH, et al. Cardiac troponin I and T for ruling out coronary artery disease in suspected chronic coronary syndrome. Sci Rep. 2022;12(1):945.

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Lazzarino AI, et al. The mediation of coronary calcification in the association between risk scores and cardiac troponin T elevation in healthy adults: is atherosclerosis a good prognostic precursor of coronary disease? Prev Med. 2015;77:150–4.

Olson F, et al. Association between high-sensitive troponin I and coronary artery calcification in a Danish general population. Atherosclerosis. 2016;245:88–93.

Giachelli CM. Vascular calcification mechanisms. J Am Soc Nephrol. 2004;15(12):2959–64.

Paana T, et al. Cardiac troponin elevations in marathon runners. Role of coronary atherosclerosis and skeletal muscle injury. The MaraCat Study. Int J Cardiol. 2019;295:25–8.

Jansen H, et al. Repeat measurements of high sensitivity troponins for the Prediction of Recurrent Cardiovascular events in patients with established Coronary Heart Disease: an analysis from the KAROLA Study. J Am Heart Assoc. 2019;8(12):e011882.

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Naghmeh Shahraki and Sara Samadi equally contributed as the first author.

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Department of Clinical Pharmacy, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran

Naghmeh Shahraki, Omid Arasteh, Batool Zarei & Amir Hooshang Mohammadpour

Department of Internal Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Sara Samadi

Department of cardiovascular diseases, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Reza Javidi Dashtbayaz

Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran

Amir Hooshang Mohammadpour

Department of Surgery, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Vahid Jomehzadeh

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All authors contributed to the study’s conception and design. N.Sh. and S.S. independently screened and extracted the data from the articles and contributed equally to this work. The first draft of the manuscript was written by N.Sh. and S.S. O.A., R.J.D., and B.Z. resolved any discrepancies during screening and data extraction. A.H.M. and V.J. revised the manuscript. All authors read and approved the final manuscript.

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Shahraki, N., Samadi, S., Arasteh, O. et al. Cardiac troponins and coronary artery calcium score: a systematic review. BMC Cardiovasc Disord 24 , 96 (2024). https://doi.org/10.1186/s12872-024-03761-x

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Cochrane Training

Chapter 7: considering bias and conflicts of interest among the included studies.

Isabelle Boutron, Matthew J Page, Julian PT Higgins, Douglas G Altman, Andreas Lundh, Asbjørn Hróbjartsson; on behalf of the Cochrane Bias Methods Group

Key Points:

  • Review authors should seek to minimize bias. We draw a distinction between two places in which bias should be considered. The first is in the results of the individual studies included in a systematic review. The second is in the result of the meta-analysis (or other synthesis) of findings from the included studies.
  • Problems with the design and execution of individual studies of healthcare interventions raise questions about the internal validity of their findings; empirical evidence provides support for this concern.
  • An assessment of the internal validity of studies included in a Cochrane Review should emphasize the risk of bias in their results, that is, the risk that they will over-estimate or under-estimate the true intervention effect.
  • Results of meta-analyses (or other syntheses) across studies may additionally be affected by bias due to the absence of results from studies that should have been included in the synthesis.
  • Review authors should consider source of funding and conflicts of interest of authors of the study, which may inform the exploration of directness and heterogeneity of study results, assessment of risk of bias within studies, and assessment of risk of bias in syntheses owing to missing results.

Cite this chapter as: Boutron I, Page MJ, Higgins JPT, Altman DG, Lundh A, Hróbjartsson A. Chapter 7: Considering bias and conflicts of interest among the included studies. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

7.1 Introduction

Cochrane Reviews seek to minimize bias. We define bias as a systematic error , or deviation from the truth, in results. Biases can lead to under-estimation or over-estimation of the true intervention effect and can vary in magnitude: some are small (and trivial compared with the observed effect) and some are substantial (so that an apparent finding may be due entirely to bias). A source of bias may even vary in direction across studies. For example, bias due to a particular design flaw such as lack of allocation sequence concealment may lead to under-estimation of an effect in one study but over-estimation in another (Jüni et al 2001).

Bias can arise because of the actions of primary study investigators or because of the actions of review authors, or may be unavoidable due to constraints on how research can be undertaken in practice. Actions of authors can, in turn, be influenced by conflicts of interest. In this chapter we introduce issues of bias in the context of a Cochrane Review, covering both biases in the results of included studies and biases in the results of a synthesis. We introduce the general principles of assessing the risk that bias may be present, as well as the presentation of such assessments and their incorporation into analyses. Finally, we address how source of funding and conflicts of interest of study authors may impact on study design, conduct and reporting. Conflicts of interest held by review authors are also of concern; these should be addressed using editorial procedures and are not covered by this chapter (see Chapter 1, Section 1.3 ).

We draw a distinction between two places in which bias should be considered. The first is in the results of the individual studies included in a systematic review . Since the conclusions drawn in a review depend on the results of the included studies, if these results are biased, then a meta-analysis of the studies will produce a misleading conclusion. Therefore, review authors should systematically take into account risk of bias in results of included studies when interpreting the results of their review.

The second place in which bias should be considered is the result of the meta-analysis (or other synthesis) of findings from the included studies . This result will be affected by biases in the included studies, and may additionally be affected by bias due to the absence of results from studies that should have been included in the synthesis. Specifically, the conclusions of the review may be compromised when decisions about how, when and where to report results of eligible studies are influenced by the nature and direction of the results. This is the problem of ‘non-reporting bias’ (also described as ‘publication bias’ and ‘selective reporting bias’). There is convincing evidence that results that are statistically non-significant and unfavourable to the experimental intervention are less likely to be published than statistically significant results, and hence are less easily identified by systematic reviews (see Section 7.2.3 ). This leads to results being missing systematically from syntheses, which can lead to syntheses over-estimating or under-estimating the effects of an intervention. For this reason, the assessment of risk of bias due to missing results is another essential component of a Cochrane Review.

Both the risk of bias in included studies and risk of bias due to missing results may be influenced by conflicts of interest of study investigators or funders . For example, investigators with a financial interest in showing that a particular drug works may exclude participants who did not respond favourably to the drug from the analysis, or fail to report unfavourable results of the drug in a manuscript.

Further discussion of assessing risk of bias in the results of an individual randomized trial is available in Chapter 8 , and of a non-randomized study in Chapter 25 . Further discussion of assessing risk of bias due to missing results is available in Chapter 13 .

7.1.1 Why consider risk of bias?

There is good empirical evidence that particular features of the design, conduct and analysis of randomized trials lead to bias on average, and that some results of randomized trials are suppressed from dissemination because of their nature. However, it is usually impossible to know to what extent biases have affected the results of a particular study or analysis (Savović et al 2012). For these reasons, it is more appropriate to consider whether a result is at risk of bias rather than claiming with certainty that it is biased. Most recent tools for assessing the internal validity of findings from quantitative studies in health now focus on risk of bias, whereas previous tools targeted the broader notion of ‘methodological quality’ (see also Section 7.1.2 ).

Bias should not be confused with imprecision . Bias refers to systematic error , meaning that multiple replications of the same study would reach the wrong answer on average. Imprecision refers to random error , meaning that multiple replications of the same study will produce different effect estimates because of sampling variation, but would give the right answer on average. Precision depends on the number of participants and (for dichotomous outcomes) the number of events in a study, and is reflected in the confidence interval around the intervention effect estimate from each study. The results of smaller studies are subject to greater sampling variation and hence are less precise. A small trial may be at low risk of bias yet its result may be estimated very imprecisely, with a wide confidence interval. Conversely, the results of a large trial may be precise (narrow confidence interval) but also at a high risk of bias.

Bias should also not be confused with the external validity of a study, that is, the extent to which the results of a study can be generalized to other populations and settings. For example, a study may enrol participants who are not representative of the population who most commonly experience a particular clinical condition. The results of this study may have limited generalizability to the wider population, but will not necessarily give a biased estimate of the effect in the highly specific population on which it is based. Factors influencing the applicability of an included study to the review question are covered in Chapter 14 and Chapter 15 .

7.1.2 From quality scales to domain-based tools

Critical assessment of included studies has long been an important component of a systematic review or meta-analysis, and methods have evolved greatly over time. Early appraisal tools were structured as quality ‘scales’, which combined information on several features into a single score. However, this approach was questioned after it was revealed that the type of quality scale used could significantly influence the interpretation of the meta-analysis results (Jüni et al 1999). That is, risk ratios of trials deemed ‘high quality’ by some scales suggested that the experimental intervention was superior, whereas when trials were deemed ‘high quality’ by other scales, the opposite was the case. The lack of a theoretical framework underlying the concept of ‘quality’ assessed by these scales resulted in tools mixing different concepts such as risk of bias, imprecision, relevance, applicability, ethics, and completeness of reporting. Furthermore, the summary score combining these components is difficult to interpret (Jüni et al 2001).

In 2008, Cochrane released the Cochrane risk-of-bias (RoB) tool, which was slightly revised in 2011 (Higgins et al 2011). The tool was built on the following key principles:

  • The tool focused on a single concept: risk of bias. It did not consider other concepts such as the quality of reporting, precision (the extent to which results are free of random errors), or external validity (directness, applicability or generalizability).
  • The tool was based on a domain-based (or component) approach, in which different types of bias are considered in turn. Users were asked to assess seven domains: random sequence generation, allocation sequence concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective outcome reporting, and other sources of bias. There was no scoring system in the tool.
  • The domains were selected to characterize mechanisms through which bias may be introduced into a trial, based on a combination of theoretical considerations and empirical evidence.
  • The assessment of risk of bias required judgement and should thus be completely transparent. Review authors provided a judgement for each domain, rated as ‘low’, ‘high’ or ‘unclear’ risk of bias, and provided reasons to support their judgement.

This tool has been implemented widely both in Cochrane Reviews and non-Cochrane reviews (Jørgensen et al 2016). However, user testing has raised some concerns related to the modest inter-rater reliability of some domains (Hartling et al 2013), the need to rethink the theoretical background of the ‘selective outcome reporting’ domain (Page and Higgins 2016), the misuse of the ‘other sources of bias’ domain (Jørgensen et al 2016), and the lack of appropriate consideration of the risk-of-bias assessment in the analyses and interpretation of results (Hopewell et al 2013).

To address these concerns, a new version of the Cochrane risk-of-bias tool, RoB 2, has been developed, and this should be used for all randomized trials in Cochrane Reviews ( MECIR Box 7.1.a ). The tool, described in Chapter 8 , includes important innovations in the assessment of risk of bias in randomized trials. The structure of the tool is similar to that of the ROBINS-I tool for non-randomized studies of interventions (described in Chapter 25 ). Both tools include a fixed set of bias domains, which are intended to cover all issues that might lead to a risk of bias. To help reach risk-of-bias judgements, a series of ‘signalling questions’ are included within each domain. Also, the assessment is typically specific to a particular result. This is because the risk of bias may differ depending on how an outcome is measured and how the data for the outcome are analysed. For example, if two analyses for a single outcome are presented, one adjusted for baseline prognostic factors and the other not, then the risk of bias in the two results may be different. 

MECIR Box 7.1.a Relevant expectations for conduct of intervention reviews

7.2 Empirical evidence of bias

Where possible, assessments of risk of bias in a systematic review should be informed by evidence. The following sections summarize some of the key evidence about bias that informs our guidance on risk-of-bias assessments in Cochrane Reviews.

7.2.1 Empirical evidence of bias in randomized trials: meta-epidemiologic studies

Many empirical studies have shown that methodological features of the design, conduct and reporting of studies are associated with biased intervention effect estimates. This evidence is mainly based on meta-epidemiologic studies using a large collection of meta-analyses to investigate the association between a reported methodological characteristic and intervention effect estimates in randomized trials. The first meta-epidemiologic study was published in 1995. It showed exaggerated intervention effect estimates when intervention allocation methods were inadequate or unclear and when trials were not described as double-blinded (Schulz et al 1995). These results were subsequently confirmed in several meta-epidemiologic studies, showing that lack of reporting of adequate random sequence generation, allocation sequence concealment, double blinding and more specifically blinding of outcome assessors tend to yield higher intervention effect estimates on average (Dechartres et al 2016a, Page et al 2016).

Evidence from meta-epidemiologic studies suggests that the influence of methodological characteristics such as lack of blinding and inadequate allocation sequence concealment varies by the type of outcome. For example, the extent of over-estimation is larger when the outcome is subjectively measured (e.g. pain) and therefore likely to be influenced by knowledge of the intervention received, and lower when the outcome is objectively measured (e.g. death) and therefore unlikely to be influenced by knowledge of the intervention received (Wood et al 2008, Savović et al 2012).

7.2.2 Trial characteristics explored in meta-epidemiologic studies that are not considered sources of bias

Researchers have also explored the influence of other trial characteristics that are not typically considered a threat to a direct causal inference for intervention effect estimates. Recent meta-epidemiologic studies have shown that effect estimates were lower in prospectively registered trials compared with trials not registered or registered retrospectively (Dechartres et al 2016b, Odutayo et al 2017). Others have shown an association between sample size and effect estimates, with larger effects observed in smaller trials (Dechartres et al 2013). Studies have also shown a consistent association between intervention effect and single or multiple centre status, with single-centre trials showing larger effect estimates, even after controlling for sample size (Dechartres et al 2011).

In some of these cases, plausible bias mechanisms can be hypothesized. For example, both the number of centres and sample size may be associated with intervention effect estimates because of non-reporting bias (e.g. single-centre studies and small studies may be more likely to be published when they have larger, statistically significant effects than when they have smaller, non-significant effects); or single-centre and small studies may be subject to less stringent controls and checks. However, alternative explanations are possible, such as differences in factors relating to external validity (e.g. participants in small, single-centre trials may be more homogenous than participants in other trials). Because of this, these factors are not directly captured by the risk-of-bias tools recommended by Cochrane. Review authors should record these characteristics systematically for each study included in the systematic review (e.g. in the ‘Characteristics of included studies’ table) where appropriate. For example, trial registration status should be recorded for all randomized trials identified.

7.2.3 Empirical evidence of non-reporting biases

A list of the key types of non-reporting biases is provided in Table 7.2.a . In the sections that follow, we provide some of the evidence that underlies this list.

Table 7.2.a Definitions of some types of non-reporting biases

7.2.3.1 Selective publication of study reports

There is convincing evidence that the publication of a study report is influenced by the nature and direction of its results (Chan et al 2014). Direct empirical evidence of such selective publication (or ‘publication bias’) is obtained from analysing a cohort of studies in which there is a full accounting of what is published and unpublished (Franco et al 2014). Schmucker and colleagues analysed the proportion of published studies in 39 cohorts (including 5112 studies identified from research ethics committees and 12,660 studies identified from trials registers) (Schmucker et al 2014). Only half of the studies were published, and studies with statistically significant results were more likely to be published than those with non-significant results (odds ratio (OR) 2.8; 95% confidence interval (CI) 2.2 to 3.5) (Schmucker et al 2014). Similar findings were observed by Scherer and colleagues, who conducted a systematic review of 425 studies that explored subsequent full publication of research initially presented at biomedical conferences (Scherer et al 2018). Only 37% of the 307,028 abstracts presented at conferences were published later in full (60% for randomized trials), and abstracts with statistically significant results in favour of the experimental intervention (versus results in favour of the comparator intervention) were more likely to be published in full (OR 1.17; 95% CI 1.07 to 1.28) (Scherer et al 2018). By examining a cohort of 164 trials submitted to the FDA for regulatory approval, Rising and colleagues found that trials with favourable results were more likely than those with unfavourable results to be published (OR 4.7; 95% CI 1.33 to 17.1) (Rising et al 2008).

In addition to being more likely than unpublished randomized trials to have statistically significant results, published trials also tend to report larger effect estimates in favour of the experimental intervention than trials disseminated elsewhere (e.g. in conference abstracts, theses, books or government reports) (ratio of odds ratios 0.90; 95% CI 0.82 to 0.98) (Dechartres et al 2018). This bias has been observed in studies in many scientific disciplines, including the medical, biological, physical and social sciences (Polanin et al 2016, Fanelli et al 2017).

7.2.3.2 Other types of selective dissemination of study reports

The length of time between completion of a study and publication of its results can be influenced by the nature and direction of the study results (‘time-lag bias’). Several studies suggest that randomized trials with results that favour the experimental intervention are published in journals about one year earlier on average than trials with unfavourable results (Hopewell et al 2007, Urrutia et al 2016).

Investigators working in a non-English speaking country may publish some of their work in local, non-English language journals, which may not be indexed in the major biomedical databases (‘language bias’). It has long been assumed that investigators are more likely to publish positive studies in English-language journals than in local, non-English language journals (Morrison et al 2012). Contrary to this belief, Dechartres and colleagues identified larger intervention effects in randomized trials published in a language other than English than in English (ratio of odds ratios 0.86; 95% CI 0.78 to 0.95), which the authors hypothesized may be related to the higher risk of bias observed in the non-English language trials (Dechartres et al 2018). Several studies have found that in most cases there were no major differences between summary estimates of meta-analyses restricted to English-language studies compared with meta-analyses including studies in languages other than English (Morrison et al 2012, Dechartres et al 2018).

The number of times a study report is cited appears to be influenced by the nature and direction of its results (‘citation bias’). In a meta-analysis of 21 methodological studies, Duyx and colleagues observed that articles with statistically significant results were cited 1.57 times the rate of articles with non-significant results (rate ratio 1.57; 95% CI 1.34 to 1.83) (Duyx et al 2017). They also found that articles with results in a positive direction (regardless of their statistical significance) were cited at 2.14 times the rate of articles with results in a negative direction (rate ratio 2.14; 95% CI 1.29 to 3.56) (Duyx et al 2017). In an analysis of 33,355 studies across all areas of science, Fanelli and colleagues found that the number of citations received by a study was positively correlated with the magnitude of effects reported (Fanelli et al 2017). If positive studies are more likely to be cited, they may be more likely to be located, and thus more likely to be included in a systematic review.

Investigators may report the results of their study across multiple publications; for example, Blümle and colleagues found that of 807 studies approved by a research ethics committee in Germany from 2000 to 2002, 135 (17%) had more than one corresponding publication (Blümle et al 2014). Evidence suggests that studies with statistically significant results or larger treatment effects are more likely to lead to multiple publications (‘multiple (duplicate) publication bias’) (Easterbrook et al 1991, Tramèr et al 1997), which makes it more likely that they will be located and included in a meta-analysis.

Research suggests that the accessibility or level of indexing of journals is associated with effect estimates in trials (‘location bias’). For example, a study of 61 meta-analyses found that trials published in journals indexed in Embase but not MEDLINE yielded smaller effect estimates than trials indexed in MEDLINE (ratio of odds ratios 0.71; 95% CI 0.56 to 0.90); however, the risk of bias due to not searching Embase may be minor, given the lower prevalence of Embase-unique trials (Sampson et al 2003). Also, Moher and colleagues estimate that 18,000 biomedical research studies are tucked away in ‘predatory’ journals, which actively solicit manuscripts and charge publications fees without providing robust editorial services (such as peer review and archiving or indexing of articles) (Moher et al 2017). The direction of bias associated with non-inclusion of studies published in predatory journals depends on whether they are publishing valid studies with null results or studies whose results are biased towards finding an effect.

7.2.3.3 Selective dissemination of study results

The need to compress a substantial amount of information into a few journal pages, along with a desire for the most noteworthy findings to be published, can lead to omission from publication of results for some outcomes because of the nature and direction of the findings. Particular results may not be reported at all ( ‘selective non-reporting of results’ ) or be reported incompletely ( ‘selective under-reporting of results’ , e.g. stating only that “P>0.05” rather than providing summary statistics or an effect estimate and measure of precision) (Kirkham et al 2010). In such instances, the data necessary to include the results in a meta-analysis are unavailable. Excluding such studies from the synthesis ignores the information that no significant difference was found, and biases the synthesis towards finding a difference (Schmid 2016).

Evidence of selective non-reporting and under-reporting of results in randomized trials has been obtained by comparing what was pre-specified in a trial protocol with what is available in the final trial report. In two landmark studies, Chan and colleagues found that results were not reported for at least one benefit outcome in 71% of randomized trials in one cohort (Chan et al 2004a) and 88% in another (Chan et al 2004b). Results were under-reported (e.g. stating only that “P>0.05”) for at least one benefit outcome in 92% of randomized trials in one cohort and 96% in another. Statistically significant results for benefit outcomes were twice as likely as non-significant results to be completely reported (range of odds ratios 2.4 to 2.7) (Chan et al 2004a, Chan et al 2004b). Reviews of studies investigating selective non-reporting and under-reporting of results suggest that it is more common for outcomes defined by trialists as secondary rather than primary (Jones et al 2015, Li et al 2018).

Selective non-reporting and under-reporting of results occurs for both benefit and harm outcomes. Examining the studies included in a sample of 283 Cochrane Reviews, Kirkham and colleagues suspected that 50% of 712 studies with results missing for the primary benefit outcome of the review were missing because of the nature of the results (Kirkham et al 2010). This estimate was slightly higher (63%) in 393 studies with results missing for the primary harm outcome of 322 systematic reviews (Saini et al 2014).

7.3 General procedures for risk-of-bias assessment

7.3.1 collecting information for assessment of risk of bias.

Information for assessing the risk of bias can be found in several sources, including published articles, trials registers, protocols, clinical study reports (i.e. documents prepared by pharmaceutical companies, which provide extensive detail on trial methods and results), and regulatory reviews (see also Chapter 5, Section 5.2 ).

Published articles are the most frequently used source of information for assessing risk of bias. This source is theoretically very valuable because it has been reviewed by editors and peer reviewers, who ideally will have prompted authors to report their methods transparently. However, the completeness of reporting of published articles is, in general, quite poor, and essential information for assessing risk of bias is frequently missing. For example, across 20,920 randomized trials included in 2001 Cochrane Reviews, the percentage of trials at unclear risk of bias was 49% for random sequence generation, 57% for allocation sequence concealment; 31% for blinding and 25% for incomplete outcome data (Dechartres et al 2017). Nevertheless, more recent trials were less likely to be judged at unclear risk of bias, suggesting that reporting is improving over time (Dechartres et al 2017).

Trials registers can be a useful source of information to obtain results of studies that have not yet been published (Riveros et al 2013). However, registers typically report only limited information about methods used in the trial to inform an assessment of risk of bias (Wieseler et al 2012). Protocols, which outline the objectives, design, methodology, statistical consideration and procedural aspects of a clinical study, may provide more detailed information on the methods used than that provided in the results report of a study. They are increasingly being published or made available by journals who publish the final report of a study. Protocols are also available in some trials registers, particularly ClinicalTrials.gov (Zarin et al 2016), on websites dedicated to data sharing such as ClinicalStudyDataRequest.com , or from drug regulatory authorities such as the European Medicines Agency. Clinical study reports are another highly useful source of information (Wieseler et al 2012, Jefferson et al 2014).

It may be necessary to contact study investigators to request access to the trial protocol, to clarify incompletely reported information or understand discrepant information available in different sources. To reduce the risk that study authors provide overly positive answers to questions about study design and conduct, we suggest review authors use open-ended questions. For example, to obtain information about the randomization process, review authors might consider asking: ‘What process did you use to assign each participant to an intervention?’ To obtain information about blinding of participants, it might be useful to request something like, ‘Please describe any measures used to ensure that trial participants were unaware of the intervention to which they were assigned’. More focused questions can then be asked to clarify remaining uncertainties.

7.3.2 Performing assessments of risk of bias   

Risk-of-bias assessments in Cochrane Reviews should be performed independently by at least two people ( MECIR Box 7.3.a ). Doing so can minimize errors in assessments and ensure that the judgement is not influenced by a single person’s preconceptions. Review authors should also define in advance the process for resolving disagreements. For example, both assessors may attempt to resolve disagreements via discussion, and if that fails, call on another author to adjudicate the final judgement. Review authors assessing risk of bias should have either content or methodological expertise (or both), and an adequate understanding of the relevant methodological issues addressed by the risk-of-bias tool. There is some evidence that intensive, standardized training may significantly improve the reliability of risk-of-bias assessments (da Costa et al 2017). To improve reliability of assessments, a review team could consider piloting the risk-of-bias tool on a sample of articles. This may help ensure that criteria are applied consistently and that consensus can be reached. Three to six papers should provide a suitable sample for this. We do not recommend the use of statistical measures of agreement (such as kappa statistics ) to describe the extent to which assessments by multiple authors were the same. It is more important that reasons for any disagreement are explored and resolved.

MECIR Box 7.3.a Relevant expectations for conduct of intervention reviews

The process for reaching risk-of-bias judgements should be transparent. In other words, readers should be able to discern why a particular result was rated at low risk of bias and why another was rated at high risk of bias. This can be achieved by review authors providing information in risk-of-bias tables to justify the judgement made. Such information may include direct quotes from study reports that articulate which methods were used, and an explanation for why such a method is flawed. Cochrane Review authors are expected to record the source of information (including the precise location within a document) that informed each risk-of-bias judgement ( MECIR Box 7.3.b ).

MECIR Box 7.3.b Relevant expectations for conduct of intervention reviews

Many results are often available in trial reports, so review authors should think carefully about which results to assess for risk of bias. Review authors should assess risk of bias in results for outcomes that are included in the ‘Summary of findings’ table ( MECIR Box 7.1.a ). Such tables typically include seven or fewer patient-important outcomes (for more details on constructing a ‘Summary of findings’ table, see Chapter 14 ).

Novel methods for assessing risk of bias are emerging, including machine learning systems designed to semi-automate risk-of-bias assessment (Marshall et al 2016, Millard et al 2016). These methods involve using a sample of previous risk-of-bias assessments to train machine learning models to predict risk of bias from PDFs of study reports, and extract supporting text for the judgements. Some of these approaches showed good performance for identifying relevant sentences to identify information pertinent to risk of bias from the full-text content of research articles describing clinical trials. A study showed that about one-third of articles could be assessed by just one reviewer if such a tool is used instead of the two required reviewers (Millard et al 2016). However, reliability in reaching judgements about risk of bias compared with human reviewers was slight to moderate depending on the domain assessed (Gates et al 2018).

7.4 Presentation of assessment of risk of bias

Risk-of-bias assessments may be presented in a Cochrane Review in various ways. A full risk-of-bias table includes responses to each signalling question within each domain (see Chapter 8, Section 8.2 ) and risk-of-bias judgements, along with text to support each judgement. Such full tables are lengthy and are unlikely to be of great interest to readers, so should generally not be included in the main body of the review. It is nevertheless good practice to make these full tables available for reference.

We recommend the use of forest plots that present risk-of-bias judgements alongside the results of each study included in a meta-analysis (see Figure 7.4.a ). This will give a visual impression of the relative contributions of the studies at different levels of risk of bias, especially when considered in combination with the weight given to each study. This may assist authors in reaching overall conclusions about the risk of bias of the synthesized result, as discussed in Section 7.6 . Optionally, forest plots or other tables or graphs can be ordered (stratified) by judgements on each risk-of-bias domain or by the overall risk-of-bias judgement for each result.

Review authors may wish to generate bar graphs illustrating the relative contributions of studies with each of risk-of-bias judgement (low risk of bias, some concerns, and high risk of bias). When dividing up a bar into three regions for this purpose, it is preferable to determine the regions according to statistical information (e.g. precision, or weight in a meta-analysis) arising from studies in each category, rather than according to the number of studies in each category.

Figure 7.4.a Forest plot displaying RoB 2 risk-of-bias judgements for each randomized trial included in a meta-analysis of mental health first aid (MHFA) knowledge scores. Adapted from Morgan et al (2018).

systematic review study characteristics table

7.5 Summary assessments of risk of bias

Review authors should make explicit summary judgements about the risk of bias for important results both within studies and across studies (see MECIR Box 7.5.a ). The tools currently recommended by Cochrane for assessing risk of bias within included studies (RoB 2 and ROBINS-I) produce an overall judgement of risk of bias for the result being assessed. These overall judgements are derived from assessments of individual bias domains as described, for example, in Chapter 8, Section 8.2 .

To summarize risk of bias across study results in a synthesis, review authors should follow guidance for assessing certainty in the body of evidence (e.g. using GRADE), as described in Chapter 14, Section 14.2.2 . When a meta-analysis is dominated by study results at high risk of bias, the certainty of the body of evidence may be rated as being lower than if such studies were excluded from the meta-analysis. Section 7.6 discusses some possible courses of action that may be preferable to retaining such studies in the synthesis.

MECIR Box 7.5.a Relevant expectations for conduct of intervention reviews

7.6 Incorporating assessment of risk of bias into analyses

7.6.1 introduction.

When performing and presenting meta-analyses, review authors should address risk of bias in the results of included studies ( MECIR Box 7.6.a ). It is not appropriate to present analyses and interpretations while ignoring flaws identified during the assessment of risk of bias. In this section we present suitable strategies for addressing risk of bias in results from studies included in a meta-analysis, either in order to understand the impact of bias or to determine a suitable estimate of intervention effect (Section 7.6.2 ). For the latter, decisions often involve a trade-off between bias and precision. A meta-analysis that includes all eligible studies may produce a result with high precision (narrow confidence interval) but be seriously biased because of flaws in the conduct of some of the studies. However, including only the studies at low risk of bias in all domains assessed may produce a result that is unbiased but imprecise (if there are only a few studies at low risk of bias).

MECIR Box 7.6.a Relevant expectations for conduct of intervention reviews

7.6.2 Including risk-of-bias assessments in analyses

Broadly speaking, studies at high risk of bias should be given reduced weight in meta-analyses compared with studies at low risk of bias. However, methodological approaches for weighting studies according to their risk of bias are not sufficiently well developed that they can currently be recommended for use in Cochrane Reviews.

When risks of bias vary across studies in a meta-analysis, four broad strategies are available to incorporate assessments into the analysis. The choice of strategy will influence which result to present as the main finding for a particular outcome (e.g. in the Abstract). The intended strategy should be described in the protocol for the review.

(1) Primary analysis restricted to studies at low risk of bias

The first approach involves restricting the primary analysis to studies judged to be at low risk of bias overall. Review authors who restrict their primary analysis in this way are encouraged to perform sensitivity analyses to show how conclusions might be affected if studies at a high risk of bias were included.

(2) Present multiple (stratified) analyses

Stratifying according to the overall risk of bias will produce multiple estimates of the intervention effect: for example, one based on all studies, one based on studies at low risk of bias, and one based on studies at high risk of bias. Two or more such estimates might be considered with equal prominence (e.g. the first and second of these). However, presenting the results in this way may be confusing for readers. In particular, people who need to make a decision usually require a single estimate of effect. Furthermore, ‘Summary of findings’ tables typically present only a single result for each outcome. On the other hand, a stratified forest plot presents all the information transparently. Though we would generally recommend stratification is done on the basis of overall risk of bias, review authors may choose to conduct subgroup analyses based on specific bias domains (e.g. risk of bias arising from the randomization process).

Formal comparisons of intervention effects according to risk of bias can be done with a test for differences across subgroups (e.g. comparing studies at high risk of bias with studies at low risk of bias), or by using meta-regression (for more details see Chapter 10, Section 10.11.4 ). However, review authors should be cautious in planning and carrying out such analyses, because an individual review may not have enough studies in each category of risk of bias to identify meaningful differences. Lack of a statistically significant difference between studies at high and low risk of bias should not be interpreted as absence of bias, because these analyses typically have low power.

The choice between strategies (1) and (2) should be based to large extent on the balance between the potential for bias and the loss of precision when studies at high or unclear risk of bias are excluded.

(3) Present all studies and provide a narrative discussion of risk of bias

The simplest approach to incorporating risk-of-bias assessments in results is to present an estimated intervention effect based on all available studies, together with a description of the risk of bias in individual domains, or a description of the summary risk of bias, across studies. This is the only feasible option when all studies are at the same risk of bias. However, when studies have different risks of bias, we discourage such an approach for two reasons. First, detailed descriptions of risk of bias in the Results section, together with a cautious interpretation in the Discussion section, will often be lost in the Authors’ conclusions, Abstract and ‘Summary of findings’ table, so that the final interpretation ignores the risk of bias and decisions continue to be based, at least in part, on compromised evidence. Second, such an analysis fails to down-weight studies at high risk of bias and so will lead to an overall intervention effect that is too precise, as well as being potentially biased.

When the primary analysis is based on all studies, summary assessments of risk of bias should be incorporated into explicit measures of the certainty of evidence for each important outcome, for example, by using the GRADE system (Guyatt et al 2008). This incorporation can help to ensure that judgements about the risk of bias, as well as other factors affecting the quality of evidence, such as imprecision, heterogeneity and publication bias, are considered appropriately when interpreting the results of the review (see Chapter 14 and Chapter 15 ).

(4) Adjust effect estimates for bias

A final, more sophisticated, option is to adjust the result from each study in an attempt to remove the bias. Adjustments are usually undertaken within a Bayesian framework, with assumptions about the size of the bias and its uncertainty being expressed through prior distributions (see Chapter 10, Section 10.13 ). Prior distributions may be based on expert opinion or on meta-epidemiological findings (Turner et al 2009, Welton et al 2009). The approach is increasingly used in decision making, where adjustments can additionally be made for applicability of the evidence to the decision at hand. However, we do not encourage use of bias adjustments in the context of a Cochrane Review because the assumptions required are strong, limited methodological expertise is available, and it is not possible to account for issues of applicability due to the diverse intended audiences for Cochrane Reviews. The approach might be entertained as a sensitivity analysis in some situations.

7.7 Considering risk of bias due to missing results

The 2011 Cochrane risk-of-bias tool for randomized trials encouraged a study-level judgement about whether there has been selective reporting, in general, of the trial results. As noted in Section 7.2.3.3 , selective reporting can arise in several ways: (1) selective non-reporting of results, where results for some of the analysed outcomes are selectively omitted from a published report; (2) selective under-reporting of data, where results for some outcomes are selectively reported with inadequate detail for the data to be included in a meta-analysis; and (3) bias in selection of the reported result, where a result has been selected for reporting by the study authors, on the basis of the results, from multiple measurements or analyses that have been generated for the outcome domain (Page and Higgins 2016).

The RoB 2 and ROBINS-I tools focus solely on risk of bias as it pertains to a specific trial result. With respect to selective reporting, RoB 2 and ROBINS-I examine whether a specific result from the trial is likely to have been selected from multiple possible results on the basis of the findings (scenario 3 above). Guidance on assessing the risk of bias in selection of the reported result is available in Chapter 8 (for randomized trials) and Chapter 25 (for non-randomized studies of interventions).

If there is no result (i.e. it has been omitted selectively from the report or under-reported), then a risk-of-bias assessment at the level of the study result is not applicable. Selective non-reporting of results and selective under-reporting of data are therefore not covered by the RoB 2 and ROBINS-I tools. Instead, selective non-reporting of results and under-reporting of data should be assessed at the level of the synthesis across studies. Both practices lead to a situation similar to that when an entire study report is unavailable because of the nature of the results (also known as publication bias). Regardless of whether an entire study report or only a particular result of a study is unavailable, the same consequence can arise: bias in a synthesis because available results differ systematically from missing results (Page et al 2018). Chapter 13 provides detailed guidance on assessing risk of bias due to missing results in a systematic review.

7.8 Considering source of funding and conflict of interest of authors of included studies

Readers of a trial report often need to reflect on whether conflicts of interest have influenced the design, conduct, analysis and reporting of a trial. It is therefore now common for scientific journals to require authors of trial reports to provide a declaration of conflicts of interest (sometimes called ‘competing’ or ‘declarations of’ interest), to report funding sources and to describe any funder’s role in the trial.

In this section, we characterize conflicts of interest in randomized trials and discuss how conflicts of interest may impact on trial design and effect estimates. We also suggest how review authors can collect, process and use information on conflicts of interest in the assessment of:

  • directness of studies to the review’s research question;
  • heterogeneity in results due to differences in the designs of eligible studies;
  • risk of bias in results of included studies;
  • risk of bias in a synthesis due to missing results.

At the time of writing, a formal Tool for Addressing Conflicts of Interest in Trials (TACIT) is being developed under the auspices of the Cochrane Bias Methods Group. The TACIT development process has informed the content of this section, and we encourage readers to check http://tacit.one for more detailed guidance that will become available.

7.8.1 Characteristics of conflicts of interest

The Institute of Medicine defined conflicts of interest as “ a set of circumstances that creates a risk that professional judgment or actions regarding a primary interest will be unduly influenced by a secondary interest” (Lo et al 2009). In a clinical trial, the primary interest is to provide patients, clinicians and health policy makers with an unbiased and clinically relevant estimate of an intervention effect. Secondary interest may be both financial and non-financial.

Financial conflicts of interest involve both financial interests related to a specific trial (for example, a company funding a trial of a drug produced by the same company) and financial interests related to the authors of a trial report (for example, authors’ ownership of stocks or employment by a drug company).

For drug and device companies and other manufacturers, the financial difference between a negative and positive pivotal trial can be considerable. For example, the mean stock price of the companies funding 23 positive pivotal oncology trials increased by 14% after disclosure of the results (Rothenstein et al 2011). Industry funding is common, especially in drug trials. In a study of 200 trial publications from 2015, 68 (38%) of 178 trials with funding declarations were industry funded (Hakoum et al 2017). Also, in a cohort of oncology drug trials, industry funded 44% of trials and authors declared conflicts of interest in 69% of trials (Riechelmann et al 2007).

The degree of funding, and the type of the involvement of industry funders, may differ across trials. In some situations, involvement includes only the provision of free study medication for a trial that has otherwise been planned and conducted independently, and funded largely, by public means. In other situations, a company fully funds and controls a trial. In rarer cases, head-to-head trials comparing two drugs may be funded by the two different companies producing the drugs.

A Cochrane Methodology Review analysed 75 studies of the association between industry funding and trial results (Lundh et al 2017). The authors concluded that trials funded by a drug or device company were more likely to have positive conclusions and statistically significant results, and that this association could not be explained by differences in risk of bias between industry and non-industry funded trials. However, industry and non-industry trials may differ in ways that may confound the association; for example due to choice of patient population, comparator interventions or outcomes. Only one of the included studies used a meta-epidemiological design and found no clear association between industry funding and the magnitude of intervention effects (Als-Nielsen et al 2003). Similar to the association with industry funding, other studies have reported that results of trials conducted by authors with a financial conflict of interest were more likely to be positive (Ahn et al 2017).

Conflicts of interest may also be non-financial (Viswanathan et al 2014). Characterizations of non-financial conflicts of interest differ somewhat, but typically distinguish between conflicts related mainly to an individual (e.g. adherence to a theory or ideology), relationships to other individuals (e.g. loyalty to friends, family members or close colleagues), or relationship to groups (e.g. work place or professional groups). In medicine, non-financial conflicts of interest have received less attention than financial conflicts of interest. In addition, financial and non-financial conflicts are often intertwined; for example, non-financial conflicts related to institutional association can be considered as indirect financial conflicts linked to employment. Definitions of what should be characterized as a ‘non-financial’ conflict of interest, and, in particular, whether personal beliefs, experiences or intellectual commitments should be considered conflicts of interest, have been debated (Bero and Grundy 2016).

It is useful to differentiate between non-financial conflicts of interest of a trial researcher and the basic interests and hopes involved in doing good trial research. Most researchers conducting a trial will have an interest in the scientific problem addressed, a well-articulated theoretical position, anticipation for a specific trial result, and hopes for publication in a respectable journal. This is not a conflict of interest but a basic condition for doing health research. However, individual researchers may lose sight of the primacy of the methodological neutrality at the heart of a scientific enquiry, and become unduly occupied with the secondary interest of how trial results may affect academic appearance or chances of future funding. Extreme examples are the publication of fabricated trial data or trials, some of which have had an impact on systematic reviews (Marret et al 2009).

Few empirical studies of non-financial conflicts of interest in randomized trials have been published, and to our knowledge there are none that assess the impact of non-financial conflicts of interest on trial results and conclusions. However, non-financial conflicts of interests have been investigated in other types of clinical research; for example, guideline authors’ specialty appears to have influenced their voting behaviour while developing guidelines for mammography screening (Norris et al 2012).

7.8.2 Conflict of interest and trial design

Core decisions on designing a trial involve defining the type of participants to be included, the type of experimental intervention, the type of comparator, the outcomes (and timing of outcome assessments) and the choice of analysis. Such decisions will often reflect a compromise between what is clinically and scientifically ideal and what is practically possible. However, when investigators have important conflicts of interest, a trial may be designed in a way that increases its chances of detecting a positive trial result, at the expense of clinical applicability. For example, narrow eligibility criteria may exclude older and frail patients, thus reducing the possibility of detecting clinically relevant harms. Alternatively, trial designers may choose placebo as a comparator despite an effective intervention being in regular use, or they may focus on short-term surrogate outcomes rather than clinically relevant long-term outcomes (Estellat and Ravaud 2012, Wieland et al 2017).

Trial design choices may be more subtle. For example, a trial may be designed to favour an experimental drug by using an inferior comparator drug when better alternatives exist (Safer 2002) or by using a low dose of the comparator drug when the focus is efficacy and a high dose of the comparator drug when the focus is harms (Mann and Djulbegovic 2013). In a typical Cochrane Review with fairly broad eligibility criteria aiming to identify and summarize all relevant trials, it is pertinent to consider the degree to which a given trial result directly relates to the question posed by the review. If all or most identified trials have narrow eligibility criteria and short-term outcomes, a review question focusing on broad patient categories and long-term effects can only be answered indirectly by the included studies. This has implications for the assessment of the certainty of the evidence provided by the review, which is addressed through the concept of indirectness in the GRADE framework (see Chapter 14, Section 14.2 ).

If results in a meta-analysis display heterogeneity, then differences in design choices that are driven by conflicts of interest may be one reason for this. Thus, conflicts of interest may also affect reflections on the certainty of the evidence through the GRADE concept of inconsistency.

7.8.3 Conflicts of interest and risk of bias in a trial’s effect estimate

Authors of Cochrane Reviews have sometimes included conflicts of interest as an ‘other source of bias’ while using the previous versions of the risk-of-bias tool (Jørgensen et al 2016). Consistent with previous versions of the Handbook , we discourage the inclusion of conflicts of interest directly in the risk-of-bias assessment. Adding conflicts of interest to the bias tool is inconsistent with the conceptual structure of the tool, which is built on mechanistically defined bias domains. Also, restricting consideration of the potential impact of conflicts of interest to a question of risk of bias in an individual trial result overlooks other important aspects, such as the design of the trial (see Section 7.8.2 ) and potential bias in a meta-analysis due to missing results (see Section 7.8.4 ).

Conflicts of interest may lead to bias in effect estimates from a trial through several mechanisms. For example, if those recruiting participants into a trial have important conflicts of interest and the allocation sequence is not concealed, then they may be more likely to subvert the allocation process to produce intervention groups that are systematically unbalanced in favour of their preferred intervention. Similarly, investigators with important conflicts of interests may decide to exclude from the analysis some patients who did not respond as anticipated to the experimental intervention, resulting in bias due to missing outcome data. Furthermore, selective reporting of a favourable result may be strongly associated with conflicts of interest (McGauran et al 2010), due to either selective reporting of particular outcome measurements or selective reporting of particular analyses (Eyding et al 2010, Vedula et al 2013). One study found that use of modified-intention-to-treat analysis and post-randomization exclusions occurred more often in trials with industry funding or author conflicts of interest (Montedori et al 2011). Accessing the trial protocol and statistical analysis plan to determine which outcomes and analyses were pre-specified is therefore especially important for a trial with relevant conflicts of interest.

Review authors should explain how consideration of conflicts of interest informed their risk-of-bias judgements. For example, when information on the analysis plans is lacking, review authors may judge the risk of bias in selection of the reported result to be high if the study investigators had important financial conflicts of interest. Conversely, if trial investigators have clearly used methods that are likely to minimize bias, review authors should not judge the risk of bias for each domain higher just because the investigators happen to have conflicts of interest. In addition, as an optional component in the revised risk-of-bias tool, review authors may reflect on the direction of bias (e.g. bias in favour of the experimental intervention). Information on conflicts of interest may inform the assessment of direction of bias.

7.8.4 Conflicts of interest and risk of bias in a synthesis of trial results

Conflicts of interest may also affect the decision not to report trial results. Conflicts of interest are probably one of several important reasons for decisions not to publish trials with negative findings, and not to publish unfavourable results (Sterne 2013). When relevant trial results are systematically missing from a meta-analysis because of the nature of the findings, the synthesis is at risk of bias due to missing results. Chapter 13 provides detailed guidance on assessing risk of bias due to missing results in a systematic review.

7.8.5 Practical approach to identifying and extracting information on conflicts of interest

When assessing conflicts of interest in a trial, review authors will, to a large degree, rely on declared conflicts. Source of funding may be reported in a trial publication, and conflicts of interest may be reported in an accompanying declaration, for example the International Committee of Medical Journal Editors ( ICMJE ) declaration. In a random sample of 1002 articles published in 2016, authors of 229 (23%) declared having a conflict of interest (Grundy et al 2018). Unfortunately, undeclared conflicts of interest and sources of funding are fairly common (Rasmussen et al 2015, Patel et al 2018).

It is always prudent to examine closely the conflicts of interest of lead and corresponding authors, based on information reported in the trial publication and the author declaration (for example, the ICMJE declaration form). Review authors should also consider examining conflicts of interest of trial co-authors and any commercial collaborators with conflicts of interest; for example, a commercial contract research organization hired by the funder to collect and analyse trial data or the involvement of a medical writing agency. Due to the high prevalence of undisclosed conflicts of interest, review authors should consider expanding their search for conflicts of interest data from other sources (e.g. disclosure in other publications by the authors, the trial protocol, the clinical study report, and public conflicts of interest registries (e.g. Open Payments database)).

We suggest that review authors balance the workload involved with the expected gain, and search additional sources of information on conflicts of interest when there is reason to suspect important conflicts of interest . As a rule of thumb, in trials with unclear funding source and no declaration of conflicts of interest from lead or corresponding authors, we suggest review authors search the Open Payments database, ClinicalTrials.gov , and conflicts of interest declarations in a few previous publications by the study authors. In trials with no commercial funding (including no company employee co-authors) and no declared conflicts of interest for lead or corresponding authors, we suggest review authors not bother to consult additional sources. Also, for trials where lead or corresponding authors have clear conflicts of interest, little additional information may be gained from checking conflicts of interest of co-authors.

Gaining access to relevant information on financial conflicts of interest is possible for a considerable number of trials, despite inherent problems of undeclared conflicts. We expect that the proportion of trials with relevant declarations will increase further.

Access to relevant information on non-financial conflicts of interest is more difficult to gain. Declaration of non-financial conflicts of interest is requested by approximately 50% of journals (Shawwa et al 2016). The term was deleted from ICMJE’s declaration in 2010 in exchange for a broad category of “Other relationships or activities” (Drazen et al 2010). Therefore, non-financial conflicts of interests are seldom self-declared, although if available, such information should be considered.

Non-financial conflicts of interest are difficult to address due to lack of relevant empirical studies on their impact on study results, lack of relevant thresholds for importance, and lack of declaration in many previous trials. However, as a rule of thumb, we suggest that review authors assume trial authors have no non-financial conflicts of interest unless there are clear suggestions of the opposite. Examples of such clues could be a considerable spin in trial publications (Boutron et al 2010), an institutional relationship pertinent to the intervention tested, or external evidence of a fixated ideological or theoretical position.

7.8.6 Judgement of notable concern about conflict of interest

Review authors should describe funding information and conflicts of interest of authors for all studies in the ‘Characteristics of included studies’ table ( MECIR Box 7.8.a ). Also, review authors may want to explore (e.g. in a subgroup analysis) whether trials with conflicts of interest have different intervention effect estimates, or more variable effect estimates, than trials without conflicts of interest. In both cases, review authors need to aim for a relevant threshold for when any conflict of interest is deemed important. If put too low, there is a risk that trivial conflicts of interest will cloud important ones; if set too high, there is the risk that important conflicts of interest are downplayed or ignored.

This judgement should take into account both the degree of conflicts of interest of study authors and also the extent of their involvement in the study. We pragmatically suggest review authors aim for a judgement about whether or not there is reason for ‘notable concern’ about conflicts of interest. This information could be displayed in a table with three columns:

  • trial identifier;
  • judgement (e.g. ‘notable concern about conflict of interest’ versus ‘no notable concern about conflict of interest’); and
  • rationale for judgement, potentially subdivided according to who had conflicts of interest (e.g. lead or corresponding authors, other authors) and stage(s) of the trial to which they contributed (design, conduct, analysis, reporting).

A judgement of ‘notable concern about conflict of interest’ should be based on reflected assessment of identified conflicts of interest. A hypothetical possibility for undeclared conflicts of interest is, as a rule of thumb, not considered sufficient reason for ‘notable concern’. By ‘notable concern’ we imply important conflicts of interest expected to have a potential impact on study design, risk of bias in study results or risk of bias in a synthesis due to missing results. For example, financial conflicts of interest are important in a trial initiated, designed, analysed and reported by drug or device company employees. Conversely, financial conflicts of interest are less important in a trial initiated, designed, analysed and reported by academics adhering to the arm’s length principle when acquiring free trial medication from a drug company, and where lead authors have no conflicts of interest. Similarly, non-financial conflicts of interest may be important in a trial of a highly controversial and ideologically loaded question such as the adverse effect of male circumcision. Non-financial conflicts of interest are less concerning in a trial comparing two treatments in general use with no connotation to highly controversial scientific theories, ideology or professional groups. Mixing trivial conflicts of interest with important ones may mask the latter and will expand review author workload considerably.

MECIR Box 7.8.a Relevant expectations for conduct of intervention reviews

7.9 Chapter information

Authors: Isabelle Boutron, Matthew J Page, Julian PT Higgins, Douglas G Altman, Andreas Lundh, Asbjørn Hróbjartsson

Acknowledgements: We thank Gerd Antes, Peter Gøtzsche, Peter Jüni, Steff Lewis, David Moher, Andrew Oxman, Ken Schulz, Jonathan Sterne and Simon Thompson for their contributions to previous versions of this chapter.

7.10 References

Ahn R, Woodbridge A, Abraham A, Saba S, Korenstein D, Madden E, Boscardin WJ, Keyhani S. Financial ties of principal investigators and randomized controlled trial outcomes: cross sectional study. BMJ 2017; 356 : i6770.

Als-Nielsen B, Chen W, Gluud C, Kjaergard LL. Association of funding and conclusions in randomized drug trials: a reflection of treatment effect or adverse events? JAMA 2003; 290 : 921-928.

Bero LA, Grundy Q. Why Having a (Nonfinancial) Interest Is Not a Conflict of Interest. PLoS Biology 2016; 14 : e2001221.

Blümle A, Meerpohl JJ, Schumacher M, von Elm E. Fate of clinical research studies after ethical approval--follow-up of study protocols until publication. PloS One 2014; 9 : e87184.

Boutron I, Dutton S, Ravaud P, Altman DG. Reporting and interpretation of randomized controlled trials with statistically nonsignificant results for primary outcomes. JAMA 2010; 303 : 2058-2064.

Chan A-W, Song F, Vickers A, Jefferson T, Dickersin K, Gøtzsche PC, Krumholz HM, Ghersi D, van der Worp HB. Increasing value and reducing waste: addressing inaccessible research. The Lancet 2014; 383 : 257-266.

Chan AW, Hróbjartsson A, Haahr MT, Gøtzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA 2004a; 291 : 2457-2465.

Chan AW, Krleža-Jeric K, Schmid I, Altman DG. Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research. Canadian Medical Association Journal 2004b; 171 : 735-740.

da Costa BR, Beckett B, Diaz A, Resta NM, Johnston BC, Egger M, Jüni P, Armijo-Olivo S. Effect of standardized training on the reliability of the Cochrane risk of bias assessment tool: a prospective study. Systematic Reviews 2017; 6 : 44.

Dechartres A, Boutron I, Trinquart L, Charles P, Ravaud P. Single-center trials show larger treatment effects than multicenter trials: evidence from a meta-epidemiologic study. Annals of Internal Medicine 2011; 155 : 39-51.

Dechartres A, Trinquart L, Boutron I, Ravaud P. Influence of trial sample size on treatment effect estimates: meta-epidemiological study. BMJ 2013; 346 : f2304.

Dechartres A, Trinquart L, Faber T, Ravaud P. Empirical evaluation of which trial characteristics are associated with treatment effect estimates. Journal of Clinical Epidemiology 2016a; 77 : 24-37.

Dechartres A, Ravaud P, Atal I, Riveros C, Boutron I. Association between trial registration and treatment effect estimates: a meta-epidemiological study. BMC Medicine 2016b; 14 : 100.

Dechartres A, Trinquart L, Atal I, Moher D, Dickersin K, Boutron I, Perrodeau E, Altman DG, Ravaud P. Evolution of poor reporting and inadequate methods over time in 20 920 randomised controlled trials included in Cochrane reviews: research on research study. BMJ 2017; 357 : j2490.

Dechartres A, Atal I, Riveros C, Meerpohl J, Ravaud P. Association between publication characteristics and treatment effect estimates: A meta-epidemiologic study. Annals of Internal Medicine 2018.

Drazen JM, de Leeuw PW, Laine C, Mulrow C, DeAngelis CD, Frizelle FA, Godlee F, Haug C, Hébert PC, Horton R, Kotzin S, Marusic A, Reyes H, Rosenberg J, Sahni P, Van der Weyden MB, Zhaori G. Towards more uniform conflict disclosures: the updated ICMJE conflict of interest reporting form. BMJ 2010; 340 : c3239.

Duyx B, Urlings MJE, Swaen GMH, Bouter LM, Zeegers MP. Scientific citations favor positive results: a systematic review and meta-analysis. Journal of Clinical Epidemiology 2017; 88 : 92-101.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet 1991; 337 : 867-872.

Estellat C, Ravaud P. Lack of head-to-head trials and fair control arms: randomized controlled trials of biologic treatment for rheumatoid arthritis. Archives of Internal Medicine 2012; 172 : 237-244.

Eyding D, Lelgemann M, Grouven U, Harter M, Kromp M, Kaiser T, Kerekes MF, Gerken M, Wieseler B. Reboxetine for acute treatment of major depression: systematic review and meta-analysis of published and unpublished placebo and selective serotonin reuptake inhibitor controlled trials. BMJ 2010; 341 : c4737.

Fanelli D, Costas R, Ioannidis JPA. Meta-assessment of bias in science. Proceedings of the National Academy of Sciences of the United States of America 2017; 114 : 3714-3719.

Franco A, Malhotra N, Simonovits G. Social science. Publication bias in the social sciences: unlocking the file drawer. Science 2014; 345 : 1502-1505.

Gates A, Vandermeer B, Hartling L. Technology-assisted risk of bias assessment in systematic reviews: a prospective cross-sectional evaluation of the RobotReviewer machine learning tool. Journal of Clinical Epidemiology 2018; 96 : 54-62.

Grundy Q, Dunn AG, Bourgeois FT, Coiera E, Bero L. Prevalence of Disclosed Conflicts of Interest in Biomedical Research and Associations With Journal Impact Factors and Altmetric Scores. JAMA 2018; 319 : 408-409.

Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, Schünemann HJ. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008; 336 : 924-926.

Hakoum MB, Jouni N, Abou-Jaoude EA, Hasbani DJ, Abou-Jaoude EA, Lopes LC, Khaldieh M, Hammoud MZ, Al-Gibbawi M, Anouti S, Guyatt G, Akl EA. Characteristics of funding of clinical trials: cross-sectional survey and proposed guidance. BMJ Open 2017; 7 : e015997.

Hartling L, Hamm MP, Milne A, Vandermeer B, Santaguida PL, Ansari M, Tsertsvadze A, Hempel S, Shekelle P, Dryden DM. Testing the risk of bias tool showed low reliability between individual reviewers and across consensus assessments of reviewer pairs. Journal of Clinical Epidemiology 2013; 66 : 973-981.

Higgins JPT, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, Savovic J, Schulz KF, Weeks L, Sterne JAC. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011; 343 : d5928.

Hopewell S, Clarke M, Stewart L, Tierney J. Time to publication for results of clinical trials. Cochrane Database of Systematic Reviews 2007; 2 : MR000011.

Hopewell S, Boutron I, Altman D, Ravaud P. Incorporation of assessments of risk of bias of primary studies in systematic reviews of randomised trials: a cross-sectional study. BMJ Open 2013; 3 : 8.

Jefferson T, Jones MA, Doshi P, Del Mar CB, Hama R, Thompson MJ, Onakpoya I, Heneghan CJ. Risk of bias in industry-funded oseltamivir trials: comparison of core reports versus full clinical study reports. BMJ Open 2014; 4 : e005253.

Jones CW, Keil LG, Holland WC, Caughey MC, Platts-Mills TF. Comparison of registered and published outcomes in randomized controlled trials: a systematic review. BMC Medicine 2015; 13 : 282.

Jørgensen L, Paludan-Muller AS, Laursen DR, Savovic J, Boutron I, Sterne JAC, Higgins JPT, Hróbjartsson A. Evaluation of the Cochrane tool for assessing risk of bias in randomized clinical trials: overview of published comments and analysis of user practice in Cochrane and non-Cochrane reviews. Systematic Reviews 2016; 5 : 80.

Jüni P, Witschi A, Bloch R, Egger M. The hazards of scoring the quality of clinical trials for meta-analysis. JAMA 1999; 282 : 1054-1060.

Jüni P, Altman DG, Egger M. Systematic reviews in health care: Assessing the quality of controlled clinical trials. BMJ 2001; 323 : 42-46.

Kirkham JJ, Dwan KM, Altman DG, Gamble C, Dodd S, Smyth R, Williamson PR. The impact of outcome reporting bias in randomised controlled trials on a cohort of systematic reviews. BMJ 2010; 340 : c365.

Li G, Abbade LPF, Nwosu I, Jin Y, Leenus A, Maaz M, Wang M, Bhatt M, Zielinski L, Sanger N, Bantoto B, Luo C, Shams I, Shahid H, Chang Y, Sun G, Mbuagbaw L, Samaan Z, Levine MAH, Adachi JD, Thabane L. A systematic review of comparisons between protocols or registrations and full reports in primary biomedical research. BMC Medical Research Methodology 2018; 18 : 9.

Lo B, Field MJ, Institute of Medicine (US) Committee on Conflict of Interest in Medical Research Education and Practice. Conflict of Interest in Medical Research, Education, and Practice . Washington, D.C.: National Academies Press (US); 2009.

Lundh A, Lexchin J, Mintzes B, Schroll JB, Bero L. Industry sponsorship and research outcome. Cochrane Database of Systematic Reviews 2017; 2 : MR000033.

Mann H, Djulbegovic B. Comparator bias: why comparisons must address genuine uncertainties. Journal of the Royal Society of Medicine 2013; 106 : 30-33.

Marret E, Elia N, Dahl JB, McQuay HJ, Møiniche S, Moore RA, Straube S, Tramèr MR. Susceptibility to fraud in systematic reviews: lessons from the Reuben case. Anesthesiology 2009; 111 : 1279-1289.

Marshall IJ, Kuiper J, Wallace BC. RobotReviewer: evaluation of a system for automatically assessing bias in clinical trials. Journal of the American Medical Informatics Association 2016; 23 : 193-201.

McGauran N, Wieseler B, Kreis J, Schuler YB, Kolsch H, Kaiser T. Reporting bias in medical research - a narrative review. Trials 2010; 11 : 37.

Millard LA, Flach PA, Higgins JPT. Machine learning to assist risk-of-bias assessments in systematic reviews. International Journal of Epidemiology 2016; 45 : 266-277.

Moher D, Shamseer L, Cobey KD, Lalu MM, Galipeau J, Avey MT, Ahmadzai N, Alabousi M, Barbeau P, Beck A, Daniel R, Frank R, Ghannad M, Hamel C, Hersi M, Hutton B, Isupov I, McGrath TA, McInnes MDF, Page MJ, Pratt M, Pussegoda K, Shea B, Srivastava A, Stevens A, Thavorn K, van Katwyk S, Ward R, Wolfe D, Yazdi F, Yu AM, Ziai H. Stop this waste of people, animals and money. Nature 2017; 549 : 23-25.

Montedori A, Bonacini MI, Casazza G, Luchetta ML, Duca P, Cozzolino F, Abraha I. Modified versus standard intention-to-treat reporting: are there differences in methodological quality, sponsorship, and findings in randomized trials? A cross-sectional study. Trials 2011; 12 : 58.

Morgan AJ, Ross A, Reavley NJ. Systematic review and meta-analysis of Mental Health First Aid training: Effects on knowledge, stigma, and helping behaviour. PloS One 2018; 13 : e0197102.

Morrison A, Polisena J, Husereau D, Moulton K, Clark M, Fiander M, Mierzwinski-Urban M, Clifford T, Hutton B, Rabb D. The effect of English-language restriction on systematic review-based meta-analyses: a systematic review of empirical studies. International Journal of Technology Assessment in Health Care 2012; 28 : 138-144.

Norris SL, Burda BU, Holmer HK, Ogden LA, Fu R, Bero L, Schunemann H, Deyo R. Author's specialty and conflicts of interest contribute to conflicting guidelines for screening mammography. Journal of Clinical Epidemiology 2012; 65 : 725-733.

Odutayo A, Emdin CA, Hsiao AJ, Shakir M, Copsey B, Dutton S, Chiocchia V, Schlussel M, Dutton P, Roberts C, Altman DG, Hopewell S. Association between trial registration and positive study findings: cross sectional study (Epidemiological Study of Randomized Trials-ESORT). BMJ 2017; 356 : j917.

Page MJ, Higgins JPT. Rethinking the assessment of risk of bias due to selective reporting: a cross-sectional study. Systematic Reviews 2016; 5 : 108.

Page MJ, Higgins JPT, Clayton G, Sterne JAC, Hróbjartsson A, Savović J. Empirical evidence of study design biases in randomized trials: systematic review of meta-epidemiological studies. PloS One 2016; 11 : 7.

Page MJ, McKenzie JE, Higgins JPT. Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review. BMJ Open 2018; 8 : e019703.

Patel SV, Yu D, Elsolh B, Goldacre BM, Nash GM. Assessment of conflicts of interest in robotic surgical studies: validating author's declarations with the open payments database. Annals of Surgery 2018; 268 : 86-92.

Polanin JR, Tanner-Smith EE, Hennessy EA. Estimating the difference between published and unpublished effect sizes: a meta-review. Review of Educational Research 2016; 86 : 207-236.

Rasmussen K, Schroll J, Gøtzsche PC, Lundh A. Under-reporting of conflicts of interest among trialists: a cross-sectional study. Journal of the Royal Society of Medicine 2015; 108 : 101-107.

Riechelmann RP, Wang L, O'Carroll A, Krzyzanowska MK. Disclosure of conflicts of interest by authors of clinical trials and editorials in oncology. Journal of Clinical Oncology 2007; 25 : 4642-4647.

Rising K, Bacchetti P, Bero L. Reporting bias in drug trials submitted to the Food and Drug Administration: review of publication and presentation. PLoS Medicine 2008; 5 : e217.

Riveros C, Dechartres A, Perrodeau E, Haneef R, Boutron I, Ravaud P. Timing and completeness of trial results posted at ClinicalTrials.gov and published in journals. PLoS Medicine 2013; 10 : e1001566.

Rothenstein JM, Tomlinson G, Tannock IF, Detsky AS. Company stock prices before and after public announcements related to oncology drugs. Journal of the National Cancer Institute 2011; 103 : 1507-1512.

Safer DJ. Design and reporting modifications in industry-sponsored comparative psychopharmacology trials. Journal of Nervous and Mental Disease 2002; 190 : 583-592.

Saini P, Loke YK, Gamble C, Altman DG, Williamson PR, Kirkham JJ. Selective reporting bias of harm outcomes within studies: findings from a cohort of systematic reviews. BMJ 2014; 349 : g6501.

Sampson M, Barrowman NJ, Moher D, Klassen TP, Pham B, Platt R, St John PD, Viola R, Raina P. Should meta-analysts search Embase in addition to Medline? Journal of Clinical Epidemiology 2003; 56 : 943-955.

Savović J, Jones HE, Altman DG, Harris RJ, Jüni P, Pildal J, Als-Nielsen B, Balk EM, Gluud C, Gluud LL, Ioannidis JPA, Schulz KF, Beynon R, Welton NJ, Wood L, Moher D, Deeks JJ, Sterne JAC. Influence of reported study design characteristics on intervention effect estimates from randomized, controlled trials. Annals of Internal Medicine 2012; 157 : 429-438.

Scherer RW, Meerpohl JJ, Pfeifer N, Schmucker C, Schwarzer G, von Elm E. Full publication of results initially presented in abstracts. Cochrane Database of Systematic Reviews 2018; 11 : MR000005.

Schmid CH. Outcome Reporting Bias: A Pervasive Problem in Published Meta-analyses. American Journal of Kidney Diseases 2016; 69 : 172-174.

Schmucker C, Schell LK, Portalupi S, Oeller P, Cabrera L, Bassler D, Schwarzer G, Scherer RW, Antes G, von Elm E, Meerpohl JJ. Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries. PloS One 2014; 9 : e114023.

Schulz KF, Chalmers I, Hayes RJ, Altman DG. Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 1995; 273 : 408-412.

Shawwa K, Kallas R, Koujanian S, Agarwal A, Neumann I, Alexander P, Tikkinen KA, Guyatt G, Akl EA. Requirements of Clinical Journals for Authors’ Disclosure of Financial and Non-Financial Conflicts of Interest: A Cross Sectional Study. PloS One 2016; 11 : e0152301.

Sterne JAC. Why the Cochrane risk of bias tool should not include funding source as a standard item [editorial]. Cochrane Database of Systematic Reviews 2013; 12 : ED000076.

Tramèr MR, Reynolds DJ, Moore RA, McQuay HJ. Impact of covert duplicate publication on meta-analysis: a case study. BMJ 1997; 315 : 635-640.

Turner RM, Spiegelhalter DJ, Smith GC, Thompson SG. Bias modelling in evidence synthesis. Journal of the Royal Statistical Society Series A, (Statistics in Society) 2009; 172 : 21-47.

Urrutia G, Ballesteros M, Djulbegovic B, Gich I, Roque M, Bonfill X. Cancer randomized trials showed that dissemination bias is still a problem to be solved. Journal of Clinical Epidemiology 2016; 77 : 84-90.

Vedula SS, Li T, Dickersin K. Differences in reporting of analyses in internal company documents versus published trial reports: comparisons in industry-sponsored trials in off-label uses of gabapentin. PLoS Medicine 2013; 10 : e1001378.

Viswanathan M, Carey TS, Belinson SE, Berliner E, Chang SM, Graham E, Guise JM, Ip S, Maglione MA, McCrory DC, McPheeters M, Newberry SJ, Sista P, White CM. A proposed approach may help systematic reviews retain needed expertise while minimizing bias from nonfinancial conflicts of interest. Journal of Clinical Epidemiology 2014; 67 : 1229-1238.

Welton NJ, Ades AE, Carlin JB, Altman DG, Sterne JAC. Models for potentially biased evidence in meta-analysis using empirically based priors. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2009; 172 : 119-136.

Wieland LS, Berman BM, Altman DG, Barth J, Bouter LM, D'Adamo CR, Linde K, Moher D, Mullins CD, Treweek S, Tunis S, van der Windt DA, Zwarenstein M, Witt C. Rating of Included Trials on the Efficacy-Effectiveness Spectrum: development of a new tool for systematic reviews. Journal of Clinical Epidemiology 2017; 84 .

Wieseler B, Kerekes MF, Vervoelgyi V, McGauran N, Kaiser T. Impact of document type on reporting quality of clinical drug trials: a comparison of registry reports, clinical study reports, and journal publications. BMJ 2012; 344 : d8141.

Wood L, Egger M, Gluud LL, Schulz K, Jüni P, Altman DG, Gluud C, Martin RM, Wood AJG, Sterne JAC. Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study. BMJ 2008; 336 : 601-605.

Zarin DA, Tse T, Williams RJ, Carr S. Trial Reporting in ClinicalTrials.gov - The Final Rule. New England Journal of Medicine 2016; 375 : 1998-2004.

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  • Published: 12 February 2024

Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis

  • Zahra Sedaghat 1 ,
  • Soheila Khodakarim 2 ,
  • Seyed Aria Nejadghaderi 3 , 4 &
  • Siamak Sabour 5  

BMC Public Health volume  24 , Article number:  444 ( 2024 ) Cite this article

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Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Controversial views exist over the effects of metabolically unhealthy obesity phenotypes on CVDs. This study aimed to perform a meta-analysis to assess the association between metabolic syndrome and myocardial infarction (MI) among individuals with excess body weight (EBW).

We searched PubMed/Medline, Scopus, and Web of Science databases as of December 9, 2023. Cohort studies involving patients with overweight or obesity that reported the relevant effect measures for the association between metabolic syndrome and MI were included. We excluded studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect sizes. We used the Newcastle Ottawa Scale for quality assessment. Random-effect model meta-analysis was performed. Publication bias was assessed by Begg’s test.

Overall, nine studies comprising a total of 61,104 participants were included. There was a significant positive association between metabolic syndrome and MI among those with obesity (hazard ratio (HR): 1.68; 95% confidence interval (CI): 1.27, 2.22). Subgroup analysis showed higher HRs for obesity (1.72; 1.03, 2.88) than overweight (1.58; 1.-13-2.21). Meta-regression revealed no significant association between nationality and risk of MI ( p  = 0.75). All studies had high qualities. There was no significant publication bias ( p  = 0.42).

Conclusions

Metabolic syndrome increased the risk of MI in those with EBW. Further studies are recommended to investigate other risk factors of CVDs in EBW, in order to implement preventive programs to reduce the burden of CVD in obesity.

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Introduction

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality in developed and developing countries and are accounting for 46.2% of total deaths worldwide [ 1 ]. As a risk factor for CVDs, metabolic syndrome is a disorder defined by the co-occurrence of at least three of five medical conditions, which are hyperglycemia, elevated triglyceride (TG), hypertension, low high-density lipoprotein (HDL), and obesity [ 2 ]. Along with lifestyle changes, metabolic syndrome is becoming a more serious health issue as the number of obese patients constantly increases among children and adults [ 3 , 4 ]. Metabolic syndrome is associated with several debilitating outcomes, such as myocardial infarction (MI), diabetes, and stroke [ 5 ]. Additionally, metabolically healthy obese individuals are at a higher risk of MI than metabolically healthy individuals with normal weight [ 6 ].

Several prior studies have been conducted to identify the association between metabolic syndrome and MI, all of which have shown that metabolic syndrome is an important risk factor for MI [ 1 , 7 , 8 ]. It is believed that lifestyles and nutritional factors, especially excess body weight (EBW) and insufficient physical activity play important roles in hypertension, hyperglycemia, dyslipidemia, and ultimately, MI development [ 1 , 9 ].

However, there are controversial findings in the studies regarding the association between metabolic syndrome and CVDs. Moreover, studies were conducted on different populations and in different settings [ 1 , 10 , 11 ]. Although several studies suggested a positive association between metabolic syndrome and MI in individuals with obesity [ 12 , 13 ], others reported contradictory results [ 14 , 15 ]. So, opinions regarding the impact of metabolic syndrome on MI in people with EBW or metabolically unhealthy obese patients are debatable. It is important to note that while meta-analyses are carried out to examine the association between metabolic syndrome and CVDs [ 16 , 17 ], none have examined the association between metabolically unhealthy obesity and MI, nor have they been published in recent years. Therefore, there is a need to conduct a pooled analysis to make a conclusive statement about the association between metabolic syndrome and CVDs in those with EBW. This systematic review and meta-analysis aimed to investigate both whether there is an association between metabolic syndrome and MI in individuals with EBW and to investigate the strength of the association using meta-analysis while reporting the pooled effect size of the association.

The study was conducted according to the guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 [ 18 ].

Study design and eligibility criteria

We included data from studies evaluated the association between metabolic syndrome and MI among participants with overweight or obesity, collectively mentioned as EBW. The PICO framework was as follow: Population: Individuals with EBW; Intervention/exposure: Diagnosis of metabolic syndrome using valid criteria; Comparison: Individuals with normal body mass index (BMI); and Outcomes: MI.

Cohort studies that evaluated the association between metabolic syndrome and MI in individuals with EBW without applying any limitation on age, sex, language, and ethnicity were included. Studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect size of the association between metabolic syndrome and outcomes of interest were excluded. Moreover, clinical trials, case reports, editorials, reviews, news, book chapters, and retracted articles were excluded. In the cases where outcomes were published at different time points, the last evaluation was considered.

Database searching and study selection

We searched electronic databases, including PubMed/Medline, Scopus, and Web of Science. Initially, keywords were selected using medical subject headings and screening of related articles and journals. Then, searches were performed separately in the databases from January 1, 2010 to June 30, 2021. We also updated the search on December 9, 2023. The detailed search quaery for each database is presented in Table S1 .

The search records were imported into the Mendeley software and deduplicated using that software. Then, two independent reviewers screened the titles and abstract. In the next step, the full-texts of the articles were retrieved and evaluated by the same reviewers. Discrepancies were resolved by consultation with the principal investigator. If the data could not be extracted from the study, we emailed the corresponding authors three times with a one week interval and asked to provide the data. If we did not receive a response or they did not provide such results, we excluded those studies.

Data extraction and risk of bias assessment

Data were extracted and summarized in a predefined data extraction form in Microsoft Excel software. In case of disagreement between the two reviewers, the third reviewer was consulted. The extracted data included study characteristics (i.e., first author’s name, publication year, follow-up, country, and study type), population characteristics (i.e., sample size, sex, age, systolic and diastolic blood pressures, fasting blood sugar (FBS), TG, HDL, low-density lipoprotein (LDL), waist circumferences, BMI, and history of smoking) and outcomes. If a study reported the results as a graph, data were extracted by “data extraction from graph method” explained by Sistrom and Mergo [ 19 ].

The risk of bias assessment was performed using the nine-star Newcastle Ottawa scale (NOS), including selection (representativeness of the population), comparability of groups (adjustment for confounders such as age and sex), and ascertainment of outcomes [ 20 ]. The NOS assigns four stars for selection, two for comparability, and three for outcome. The NOS scores of more than seven were acknowledged as high quality [ 20 ].

Statistical analysis

The STATA version 14.0 (Stata Corporation, College Station, TX) was used for statistical analysis. We used the “metan” command to perform a pooled analysis (a random or fixed effect analysis based on the heterogeneity among studies). Findings were presented as an overall hazard ratio (HR) with a 95% confidence interval (95% CI). Heterogeneity among studies was assessed using the Q-statistic and I-square test, and p -values less than 0.05 or I-square > 50% were considered as high heterogeneity. In case of high heterogeneity, subgroup analysis and meta-regression were used to investigate the potential source of heterogeneity. Funnel plot was only used to evaluate publication bias if at least ten studies were included [ 21 ]. Also, Begg's test was used to identify publication bias [ 22 ].

The search found 2898 results. Following removing 963 duplicates, 1935 articles were included for the title/abstract screening. Then, 113 studies were included for the full-text reviewing. Finally, the data from nine studies were included in the meta-analysis [ 6 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Eighty studies were excluded because they were not conducted on individuals with EBW and 24 studies were excluded because the adjusted effect sizes were not reported (Fig.  1 ).

figure 1

Study selection process

Study characteristics

These studies included 61104 participants from eight different countries and regions. The follow-up duration ranged from one to 11.6 years. The studies were published between 2010 and 2023. Three studies used adult treatment panel III (ATP-III) [ 25 , 26 , 27 ], while others used other definitions like Japanese society of internal medicine [ 6 ], American heart association/National heart, lung, and blood institute [ 24 ], harmonized international diabetes federation (IDF) [ 23 ], World Health Organization [ 28 ], joint interim statement [ 29 ], and IDF [ 30 ] (Table  1 ).

The study by Ogorodnikova et al. [ 25 ] was conducted on obese participants (BMI: 33.7 kg/m2) compared to the study by Lee et al. which was conducted on people with overweight [ 27 ]. The average of TG was lower in the study by Ogorodnikova et al. than Lee et al. (96.0 mg/dl vs. 189.9 mg/dl). Systolic blood pressures (SBPs) were 122.5, 131.8, and 150.0 mmHg in the studies by Ogorodnikova et al., Lee et al., and Thomsen et al., respectively [ 25 , 26 , 27 ]. FBS was 95.3 mg/dl in the Ogorodnikova’s study [ 25 ] compared to 179.2 mg/dl in Lee’s study [ 27 ] and 97.0 mg/dl in Thomsen’s study [ 26 ]. In addition, HDL values were 56.8, 39.6, and 46.0 mg/dl in Ogorodnikova et al., Lee et al., and Thomsen et al., respectively [ 25 , 26 , 27 ] (Table  2 ).

Quality assessment and publication bias

All studies had a high quality. The quality assessment scores were seven in three studies, eight in five studies, and nine in one study [ 27 ]. All studies had a high quality regarding selection of non-exposed cohorts, ascertainment of exposure, controlling for confounders, and duration of follow-up. The risk of bias assessment showed that seven studies did not report data regarding report the adequacy of a follow-up cohort (Table  3 ).

The Begg's test showed no significant publication bias ( p  = 0.42).

Overall meta-analysis results

We found a significant positive association between metabolic syndrome and MI among obese patients (HR = 1.68; 95% CI: 1.27, 2.22). Among nine studies included in the analysis, only one study showed a significant negative association between metabolic syndrome and MI (HR = 0.59; 95% CI: 0.47, 0.73) (Fig.  2 ).

figure 2

Forest plots of the association between metabolic syndrome and myocardial infarction among individuals with excess body weight. ES: effect size; CI: confidence interval

Subgroup analysis and meta-regression

We performed subgroup analysis by quality assessment scores and BMI values. The pooled HRs for overweight (25 < BMI ≤ 29.9 kg/m2) and obesity (BMI ≥ 30 kg/m2) were 1.58 (95% CI: 1.13, 2.21) and 1.72 (95% CI: 1.03, 2.88), respectively (Fig.  3 A). Subgroup analysis by quality assessment scores showed higher pooled HRs for score eight (1.72; 95% CI: 1.03, 2.88) than score seven (1.66; 95% CI: 1.31, 2.09) (Fig.  3 B). The meta-regression showed no significant association between nationality and risk of MI ( p  = 0.75).

figure 3

Forest plots of the association between metabolic syndrome and myocardial infarction among individuals with excess body weight by body mass index values ( A ) and quality assessment scores ( B ). ES: effect size; CI: confidence interval

To the best of our knowledge, no previous meta-analyses have assessed the association between metabolic syndrome and MI among individuals with EBW. Our results suggested that metabolic syndrome increased the risk of MI by 1.68 times among patients with EBW. The effect size was higher for obesity compared with overweight.

Among the nine studies included, only one study reported a negative association between metabolic syndrome and MI in patients with EBW [ 25 ]. In this regard, the article by Lavie and colleagues proposed a debate that some studies showed a better prognosis for CVDs in people with EBW than those with normal weights [ 31 ]. Nevertheless, the overall findings of our meta-analysis showed a significant higher risk of MI in people with EBW and metabolic syndrome. Also, previous studies showed adverse effects of metabolic syndrome. Accordingly, metabolic syndrome increased the risk of major adverse cardiovascular events by 1.55 times (95% CI: 1.28, 1.87) in patients with hypertension [ 32 ]. Another meta-analysis on eight studies showed that patients with end-stage renal disease and metabolic syndrome had an increased risk of mortality (risk ratio (RR): 1.92; 95% CI: 1.15, 3.21) and CVDs (RR: 6.42; 95% CI: 2.00, 20.58) compared to those without metabolic syndrome [ 33 ]. Therefore, it appears that metabolic syndrome has remarkable negative effects on risk of MI. Nevertheless, other large scale studies on people with EBW are recommended.

We found a high heterogeneity between studies (I-square: 92.7%). To account for the source of heterogeneity, we performed meta-regression and subgroup analysis. Meta-regressions showed no significant association with nationality. Also, subgroup analysis by quality assessment and BMI determined no source for heterogeneity. So, this heterogeneity might be related to the received treatments and relevant drugs that were not specifically reported in the primary studies. In this regard, the paper by Ogorodnikova et al. mentioned that the components of metabolic syndrome were controlled through medications [ 25 ].

It is worth noticing that people who are involved in the cohort studies might be different from healthy people in the general population because those who participated in the cohort study are under both drug and non-drug treatment, especially in obese patients. In addition, people with obesity are more taken under control, and their disease is under treatment. Due to this fact, metabolic syndrome is a protective factor for CVDs in this study. Interestingly, among different factors, the country is considered an important special contributor to that protective association. It is noticeable that the pattern of obesity is different among different countries [ 34 ]. For example, the average BMI in the United States is higher than other countries [ 35 ]. In that regard, patients with metabolic syndrome who reside in China, Japan, and Korea may not need any treatment although they have symptoms of metabolic syndrome. As a result, the severity of metabolic syndrome varies from one country to another [ 36 ]. Considering all these explanations, they did not require any drug treatments due to the early diagnosis of participants’ metabolic syndrome at the primary stages. On the other hand, the severity of metabolic syndrome in the United States was high, and all patients underwent drug treatments. Accordingly, this might explain the reasons for the protective results found in the study by Ogorodnikova et al., which was conducted in the United States [ 25 ].

Strengths and limitations

The strength of the study lies in that it is one of the pioneer studies that was focused on people with EBW and evaluated the association between metabolic syndrome and MI among them. We used a robust meta-analytical approach to report the pooled effect size for this association. Also, our included cohort studies were of high quality.

Additionally, the issue of confounders was controlled by including only cohort studies and using adjusted HRs in the analysis. So, the findings can be valuable for health policymaking and clinicians for prevention and reduction the mortality and morbidity of CVDs, particularly MI, in individuals with EBW.

Nevertheless, this systematic review and meta-analysis has some limitations that need to be taken into consideration when interpreting the results. First, the number of studies included in this meta–analysis was low. Therefore, we could not assess the publication bias using a funnel plot. Moreover, there was a high heterogeneity. To find the potential sources of heterogeneity, we performed subgroup analysis and meta-regression. However, due to the small sample number of included studies, the heterogeneities remained high. Second, a large proportion of studies did not provide sufficient information about the effect sizes among participants, leading to their exclusion. Third, although the included studies performed adjusted analysis based on several factors, there is still a possibility of biases due to inadequate adjustment for confounders. Fourth, in most primary studies, medical records were used for data gathering, raising the possibility of misclassification. Although we searched three major online databases, we did not perform grey literature search, thus potentially missing unpublished data.

Overall, metabolic syndrome significantly increased the risk of MI by 68% among individuals with EBW. Therefore, the findings of the study can be used by health policymakers to develop preventive programs for patients with EBW. Also, physicians should control the relevant risk factors, especially metabolic syndrome, in order to prevent from MI in individuals with EBW. Further large-scale observational studies and meta-analyses are needed to determine other risk factors of CVDs in patients with EBW, especially in other countries and populations like African countries and the African American race.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to decision of the research team but are available from the corresponding author on reasonable request.

Abbreviations

Cardiovascular disease

High-density lipoprotein

  • Myocardial infarction

Excess body weight

Body mass index

Fasting blood sugar

Triglyceride

Low-density lipoprotein

Newcastle Ottawa scale

Hazard ratio

Confidence interval

Adult treatment panel III

International diabetes federation

Li X, Zhai Y, Zhao J, He H, Li Y, Liu Y et al. Impact of Metabolic Syndrome and It’s Components on Prognosis in Patients With Cardiovascular Diseases: A Meta-Analysis. 2021. p. 704145-.

Sarrafzadegan N, Gharipour M, Sadeghi M, Nezafati P, Talaie M, Oveisgharan S, et al. Metabolic syndrome and the risk of ischemic stroke. J Stroke Cerebrovasc Dis. 2017;26(2):286–94.

Article   PubMed   Google Scholar  

Popa S, Moţa M, Popa A, Moţa E, Serafinceanu C, Guja C, et al. Prevalence of overweight/obesity, abdominal obesity and metabolic syndrome and atypical cardiometabolic phenotypes in the adult Romanian population: PREDATORR study. J Endocrinol Investig. 2016;39(9):1045–53.

Article   CAS   Google Scholar  

He F, Rodriguez-Colon S, Fernandez-Mendoza J, Vgontzas AN, Bixler EO, Berg A, et al. Abdominal obesity and metabolic syndrome burden in adolescents-penn state children cohort study. J Clin Densitometry. 2015;18(1):30–6.

Article   Google Scholar  

Lovic MB, Djordjevic DB, Tasic IS, Nedeljkovic IP. Impact of metabolic syndrome on clinical severity and long-term prognosis in patients with myocardial infarction with ST-segment elevation. Hellenic J Cardiol. 2018;59(4):226–31.

Hirokawa W, Nakamura K, Sakurai M, Morikawa Y, Miura K, Ishizaki M, et al. Mild metabolic abnormalities, abdominal obesity and the risk of cardiovascular diseases in middle-aged Japanese men. J Atheroscler Thromb. 2010;17(9):934–43.

Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Disease. 2016;5:2048004016633371.

Cheong KC, Lim KH, Ghazali SM, Teh CH, Cheah YK, Baharudin A et al. Association of metabolic syndrome with risk of cardiovascular disease mortality cause mortality among Malaysian adults: a retrospective cohort study. 2021:1–9.

Nejadghaderi SA, Grieger JA, Karamzad N, Kolahi A-A, Sullman MJM, Safiri S, et al. Burden of diseases attributable to excess body weight in the Middle East and North Africa region, 1990–2019. Sci Rep. 2023;13(1):20338.

Article   PubMed   PubMed Central   Google Scholar  

Caleyachetty R, Thomas GN, Toulis KA, Mohammed N, Gokhale KM, Balachandran K, et al. Metabolically healthy obese and Incident Cardiovascular Disease events among 3.5 million men and women. J Am Coll Cardiol. 2017;70(12):1429–37.

Yeh T-l, Chen H-h, Tsai S-y, Lin C-y, Liu S-j. Chien K-l. The relationship between Metabolically Healthy Obesity and the risk of Cardiovascular Disease: a systematic review and Meta-analysis. 2019(Cvd):1–15.

Hinnouho GM, Czernichow S, Dugravot A, Nabi H, Brunner EJ, Kivimaki M, et al. Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: the Whitehall II cohort study. Eur Heart J. 2015;36(9):551–9.

Hosseinpanah F, Tasdighi E, Barzin M, Mahdavi M, Ghanbarian A, Valizadeh M et al. The association between transition from metabolically healthy obesity to metabolic syndrome, and incidence of cardiovascular disease: Tehran lipid and glucose study. PLoS ONE. 2020;15(9 September).

Lavie CJ, Milani RV, Ventura HO. Disparate effects of metabolically healthy obesity in coronary heart disease and heart failure. Elsevier USA; 2014. pp. 1079–81.

Oh CM, Park JH, Chung HS, Yu JM, Chung W, Kang JG, et al. Effect of body shape on the development of cardiovascular disease in individuals with metabolically healthy obesity. Medicine. 2020;99(38):e22036–e.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Salari N, Doulatyari PK, Daneshkhah A, Vaisi-Raygani A, Jalali R, Jamshidi Pk, et al. The prevalence of metabolic syndrome in cardiovascular patients in Iran: a systematic review and meta-analysis. Diabetol Metab Syndr. 2020;12(1):96.

Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.

Matthew JP, Joanne EM, Patrick MB, Isabelle B, Tammy CH, Cynthia DM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Google Scholar  

Sistrom CL, Mergo PJ. A simple method for obtaining Original data from published graphs and plots. Am J Roentgenol. 2000;174(5):1241–4.

Wells G, Shea B, O’Connell D, Peterson j, Welch V, Losos M et al. The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. ᅟ. 2000;&#4447.

Jonathan ACS, Alex JS, John PAI, Norma T, David RJ, Joseph L, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.

Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101.

Article   CAS   PubMed   Google Scholar  

Xu Y, Li H, Wang A, Su Z, Yang G, Luo Y, et al. Association between the metabolically healthy obese phenotype and the risk of myocardial infarction: results from the Kailuan study. Eur J Endocrinol. 2018;179(6):343–52.

Simons LA, Simons J, Friedlander Y, McCallum J. Is prediction of cardiovascular disease and all-cause mortality genuinely driven by the metabolic syndrome, and independently from its component variables? The Dubbo study. Heart Lung Circ. 2011;20(4):214–9.

Ogorodnikova AD, Kim M, McGinn AP, Muntner P, Khan U, Wildman RP. Incident cardiovascular disease events in metabolically benign obese individuals. Obes (Silver Spring). 2012;20(3):651–9.

Thomsen M, Nordestgaard BG. Myocardial infarction and ischemic heart disease in overweight and obesity with and without metabolic syndrome. JAMA Intern Med. 2014;174(1):15–22.

Lee SH, Jeong MH, Kim JH, Kim MC, Sim DS, Hong YJ, et al. Influence of obesity and metabolic syndrome on clinical outcomes of ST-segment elevation myocardial infarction in men undergoing primary percutaneous coronary intervention. J Cardiol. 2018;72(4):328–34.

Sánchez-Iñigo L, Navarro-González D, Fernández-Montero A, Pastrana-Delgado J, Martínez JA. Risk of incident ischemic stroke according to the metabolic health and obesity states in the vascular-metabolic CUN cohort. Int J Stroke. 2017;12(2):187–91.

Ding J, Chen X, Shi Z, Bai K, Shi S. Association of Metabolically Healthy Obesity and risk of Cardiovascular Disease among adults in China: a retrospective cohort study. Diabetes Metab Syndr Obes. 2023;16:151–9.

Opio J, Wynne K, Attia J, Hancock S, Oldmeadow C, Kelly B, et al. Overweight or obesity increases the risk of cardiovascular disease among older Australian adults, even in the absence of cardiometabolic risk factors: a bayesian survival analysis from the Hunter Community Study. Int J Obes (Lond). 2023;47(2):117–25.

Lavie CJ, Milani RV, Ventura HO, Obesity, Disease C. Risk factor, Paradox, and impact of weight loss. J Am Coll Cardiol. 2009;53(21):1925–32.

Liu J, Chen Y, Cai K, Gong Y. Association of metabolic syndrome with cardiovascular outcomes in hypertensive patients: a systematic review and meta-analysis. J Endocrinol Investig. 2021;44(11):2333–40.

Sanguankeo A, Upala S. Metabolic syndrome increases mortality risk in Dialysis patients: a systematic review and Meta-analysis. Int J Endocrinol Metab. 2018;16(2):e61201.

PubMed   PubMed Central   Google Scholar  

Gallus S, Lugo A, Murisic B, Bosetti C, Boffetta P, La Vecchia C. Overweight and obesity in 16 European countries. Eur J Nutr. 2015;54(5):679–89.

Sanyaolu A, Okorie C, Qi X, Locke J, Rehman S. Childhood and adolescent obesity in the United States: a Public Health concern. Glob Pediatr Health. 2019;6:2333794x19891305.

Ansarimoghaddam A, Adineh HA, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: Meta-analysis of cross-sectional studies. Diabetes Metab Syndr. 2018;12(2):195–201.

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Acknowledgements

This study is related to the project of a student from Shahid Beheshti University of Medical Sciences, Tehran, Iran.

The present study was financially supported by Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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Student Research Center, Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Zahra Sedaghat

Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

Soheila Khodakarim

School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Seyed Aria Nejadghaderi

Systematic Review and Meta-analysis Expert Group (SRMEG), Universal Scientific Education and Research Network (USERN), Tehran, Iran

Department of Clinical Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Siamak Sabour

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S. Khodakarim and S. Sabour contributed in conception and design of the work; data analysis was performed by Z. Sedaghat. The first draft of the manuscript was written by Z. Sedaghat and S.A. Nejadghaderi. It was critically revised by S.A. Nejadghaderi. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Sedaghat, Z., Khodakarim, S., Nejadghaderi, S. et al. Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis. BMC Public Health 24 , 444 (2024). https://doi.org/10.1186/s12889-024-17707-7

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  • Metabolic syndrome
  • Systematic review
  • Meta-analysis

BMC Public Health

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systematic review study characteristics table

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  • Published: 07 February 2024

Procalcitonin for the diagnosis of postoperative bacterial infection after adult cardiac surgery: a systematic review and meta-analysis

  • Davide Nicolotti 1 ,
  • Silvia Grossi 1 ,
  • Valeria Palermo 1 ,
  • Federico Pontone 1 ,
  • Giuseppe Maglietta 2 ,
  • Francesca Diodati 2 ,
  • Matteo Puntoni 2 ,
  • Sandra Rossi 1   na1 &
  • Caterina Caminiti 2   na1  

Critical Care volume  28 , Article number:  44 ( 2024 ) Cite this article

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Background and aims

Patients undergoing cardiac surgery are subject to infectious complications that adversely affect outcomes. Rapid identification is essential for adequate treatment. Procalcitonin (PCT) is a noninvasive blood test that could serve this purpose, however its validity in the cardiac surgery population is still debated. We therefore performed a systematic review and meta-analysis to estimate the accuracy of PCT for the diagnosis of postoperative bacterial infection after cardiac surgery.

We included studies on adult cardiac surgery patients, providing estimates of test accuracy. Search was performed on PubMed, EmBase and WebOfScience on April 12th, 2023 and rerun on September 15th, 2023, limited to the last 10 years. Study quality was assessed with the QUADAS-2 tool. The pooled measures of performance and diagnostic accuracy, and corresponding 95% Confidence Intervals (CI), were calculated using a bivariate regression model. Due to the variation in reported thresholds, we used a multiple-thresholds within a study random effects model for meta-analysis (diagmeta R-package).

Eleven studies were included in the systematic review, and 10 (2984 patients) in the meta-analysis. All studies were single-center with observational design, five of which with retrospective data collection. Quality assessment highlighted various issues, mainly concerning lack of prespecified thresholds for the index test in all studies. Results of bivariate model analysis using multiple thresholds within a study identified the optimal threshold at 3 ng/mL, with a mean sensitivity of 0.67 (0.47–0.82), mean specificity of 0.73 (95% CI 0.65–0.79), and AUC of 0.75 (IC95% 0.29–0.95). Given its importance for practice, we also evaluated PCT’s predictive capability. We found that positive predictive value is at most close to 50%, also with a high prevalence (30%), and the negative predictive value was always > 90% when prevalence was < 20%.

Conclusions

These results suggest that PCT may be used to help rule out infection after cardiac surgery. The optimal threshold of 3 ng/mL identified in this work should be confirmed with large, well-designed randomized trials that evaluate the test’s impact on health outcomes and on the use of antibiotic therapy.

PROSPERO Registration number CRD42023415773. Registered 22 April 2023.

Graphical abstract

systematic review study characteristics table

Introduction

One of the major complications that can occur after cardiac surgery is postoperative infection, including pneumonia, surgical site infection, Clostridioides difficile colitis, and blood stream infections [ 1 ]. These complications have a reported incidence of 5–21%, and are associated with unfavorable outcomes, such as delayed hospital discharge, prolonged recovery, and a five-time increase in the postoperative death rate [ 2 ]. Timely and accurate diagnosis of postsurgical infective complications is essential, both to ensure prompt treatment to affected patients, and to avoid the use of antibiotics when not necessary [ 3 , 4 , 5 ]. Unfortunately this task can be challenging, since many typical signs of infection are nonspecific and common in the critically ill [ 4 , 5 ]. Specifically, cardiac surgery with cardiopulmonary bypass (CPB) induces an acute inflammatory response that may lead to a systemic inflammatory response syndrome (SIRS), which may mimic the typical clinical and biological manifestations of infection [ 6 ].

Conventional diagnostic tests for infection (such as blood cultures and inflammatory markers) have important limitations, particularly concerning suboptimal sensitivity and specificity [ 7 , 8 ]. In particular, microbiological cultures, generally considered the most reliable diagnostic method for identification of pathogens, provide important information on type of microorganism and susceptibility toward antibiotic treatment, but test results take a long time to be available, and are characterized by a high proportion of false negatives [ 9 ].

In the quest for a highly specific test yielding rapid results, host biological biomarkers are receiving increasing attention [ 9 ]. One of these is procalcitonin (PCT), the peptide precursor to calcitonin. PCT is released from thyroid C glands at very low levels under normal physiological conditions, but its synthesis can be greatly increased in response to infection and inflammation [ 8 ]. The use of PCT as a diagnostic marker for infection has been established in specific settings; the United States Food and Drug Administration has approved its use for initiating or discontinuing antibiotics in lower respiratory tract infections and for discontinuing antibiotics in patients with sepsis [ 8 ]. However, the use of PCT for prescribing antimicrobial medications in septic patients has been questioned and is not recommended by recent guidelines [ 10 , 11 ]. Concerning applications in surgery, some meta-analyses have investigated the diagnostic accuracy of PCT for postoperative infection on different populations, such as major gastrointestinal surgery [ 12 ], liver transplantation [ 13 ], colorectal surgery [ 14 ], and solid organ transplantation [ 15 ], reporting mixed results. To our knowledge, the only existing meta-analysis on the diagnostic accuracy of PCT for infection post-cardiac surgery including adult patients was performed in 2021 by Li et al. [ 16 ]. This work included 14 studies published between 2000 and 2017, and considered both children (six articles) and adults (eight articles). The authors concluded that PCT was a promising marker for the diagnosis of sepsis for cardiac surgery patients. However, the inclusion of children may have amplified the effect, since in pediatric patients mean postoperative PCT values are markedly higher after cardiac surgery [ 17 ].

Based on the above considerations, we performed a systematic review and meta-analysis to evaluate the accuracy of PCT for the diagnosis of postoperative bacterial infection in patients undergoing cardiac surgery. We restricted inclusion to studies on adult subjects and applied stringent eligibility criteria for the diagnosis of the target condition, to reduce heterogeneity.

Before commencing this work, the PROSPERO database [ 18 ] was searched in March 2023, to identify any ongoing review with the same study question, but none was found. This review was designed and conducted following the Preferred Reporting for Systematic reviews and Meta-Analyses (PRISMA) [ 19 ] and the Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) [ 20 ] guidelines. The protocol was registered with PROSPERO (CRD42023415773) on 22 April 2023.

Criteria for considering studies for this review

Types of studies.

We considered studies evaluating the diagnostic accuracy of PCT (index test) for postoperative bacterial infection (target condition) among adult patients undergoing cardiac surgery. Studies were eligible if they produced estimates of test accuracy or provided 2 × 2 data (true positive (TP), false positive (FP), true negative (TN), false negative (FN)) from which estimates for the primary objective could be computed.

We excluded studies with fewer than 10 participants and single case reports, as well as literature reviews, editorial material, and meeting abstracts. Inclusion was restricted to reports published from January 1st, 2013 to September 15th, 2023, to better reflect the current situation, where improvements in standards of care have led to a decrease in surgery-related stress, and thus of the occurrence of SIRS, which may be misclassified as bacterial infection.

Population eligibility

Studies had to concern adult patients (age ≥ 18 years) undergoing surgery of the heart or ascending aorta/aortic arch, with or without the use of CPB, regardless of type of surgical access site, and without infection before surgery. Subjects undergoing transcatheter interventions were also excluded.

PCT, measured at least once after surgery using any kit and method of assay. We reported these index tests as positive or negative on the basis of study threshold cutoffs.

Target condition

Any postoperative bacterial infection. Diagnosis had to be made according to clearly defined criteria, such as the ones established by the Centers for Disease Control [ 21 ], to ensure that a predetermined reference standard was used.

Search strategy and literature selection

The search strategies were developed by an information specialist (FD), in close collaboration with the clinicians in the research team. MedLine (PubMed platform), EmBase, and Web Of Science Clarivate were searched, with no language restrictions, from 2013 to present. The original search was performed on April 12th, 2023, and rerun on September 15th, 2023. A “backwards” snowball search was conducted on the references of systematic reviews and relevant papers. The full search strategies for each database together with notes on their development are provided in Additional file 1 : Table S1.

Title and abstract screening was performed independently by two reviewers (DN and VP) using the Rayyan platform [ 22 ] and discrepancies were resolved by consulting a third reviewer (CC). Next, two reviewers (SG and FP) independently examined the full texts of the screened publications to determine eligibility with respect to protocol criteria. Again, disagreements were resolved by a third independent reviewer (CC).

Data extraction

Information on diagnostic accuracy from eligible papers was extracted by two researchers independently (CC and GM), using a Microsoft Excel form, and disagreements were resolved through discussion, involving a third reviewer when necessary (MP).

When the numbers of TP, FP, TN, and FN were not available, we extracted them based on the provided indices of Sensitivity (Se), Specificity (Sp), and sample size values.

Study investigators were contacted when data confirmation was needed.

Assessment of methodological quality

Methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist [ 23 ], recommended by the Cochrane collaboration for the quality assessment of diagnostic studies. The QUADAS-2 tool comprises four domains: patient selection, index test, reference standard, flow and timing, and enables to rate both risk of bias of included studies and their applicability to the review question. Signaling questions are provided to help reach judgments on risk of bias. Quality assessment was performed independently by two reviewers (CC and FD), and conflicts resolved by a third reviewer (MP). Risk of bias in QUADAS-2 is judged as “low”, “high”, or “unclear”. Following the instrument’s manual [ 24 ], risk of bias was judged “low” when all signaling questions for a domain were answered “yes”. If any signaling question was answered “no”, reviewers discussed the potential for bias. We did not construct funnel plots, because in meta-analyses of diagnostic studies, statistical tests based on funnel plot asymmetry do not allow to discriminate between publication bias and other sources of asymmetry, like the effect of including multiple thresholds [ 25 ].

Statistical analysis and data synthesis

We planned to perform the meta-analysis if four or more studies were available. Classification tables (TP, FP, TN, FN) were extracted or reconstructed to calculate the performance of the index biomarker. The included studies contributed varying numbers of test days and postoperative thresholds, as well as different thresholds on the same day. For the analyses, we extracted accuracy data on all cut-off points for which the data was available or could be calculated.

Estimates of SE, SP, and corresponding 95% Confidence Intervals (CI) for each study were graphically illustrated in forest plots.

The pooled diagnostic accuracy (Se, Sp, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR)), were calculated using a bivariate model [ 26 ] accounting for within- and between-study variance. This model creates a link between the range of thresholds and the respective pairs of sensitivity and specificity, and thus allows to identify thresholds at which the test is likely to perform best. We used PLR and NLR as an indication of clinical informativeness. A PLR greater than 1 indicates that a positive test is associated with an increase in the likelihood of an infection being present. A NLR less than 1 indicates that a negative test is associated with a decrease in the likelihood of an infection. Furthermore, likelihood ratios above 10 and below 0.1 are considered to provide strong evidence to rule in or rule out diagnoses, respectively[ 27 ]. The DOR is a measure of discriminatory test performance that compares the odds of positivity in a disease state to the odds of positivity in a non-disease state, with higher values indicating better performance [ 28 ]. Bivariate model analysis using multiple thresholds within a study enabled to determine an optimal threshold and a Summary Receiver Operating Characteristic (SROC) curve and the corresponding Area Under the Curve (AUC) [ 29 ]. Since heterogeneity is to be expected in meta-analyses of diagnostic test accuracy, random effects methods were used. Furthermore, by considering the varying thresholds per day, interaction terms (threshold* day) were added and analyzed with the bivariate model analysis using multiple thresholds within a study.

Finally, for clinical practice, it is necessary to know the probability of a patient having a postoperative bacterial infection or not when the PCT test result exceeds a certain threshold. To address this issue, we also used the bivariate multiple-threshold model and calculated Negative Predictive Value (NPV) and Positive Predictive Value (PPV), relative to a simulated range of threshold values (1 to 5) for different prevalence levels (5–30%).

All Statistical analysis were performed with R for Windows (Version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) with madad and diagmeta packages.

Analysis of subgroups or subsets

We did not carry out any of the subgroup and additional outcome analyses planned in the protocol, due to the small number of studies or to the absence of the necessary information in study reports. For the same reasons, no sensitivity analysis was performed.

We assessed statistical heterogeneity for nonthreshold effect using I 2 and the Cochrane Q test based on random effects analysis. I 2  > 50% and the p value ≤ 0.05 were considered significant heterogeneity. For threshold effects, the heterogeneity was calculated by the visual inspection from the SROC curve [ 30 , 31 , 32 ].

Study selection

The PRISMA flow diagram for identification, screening, and inclusion of studies is shown in Fig.  1 .

figure 1

Study flow diagram

The original search performed on April 12th 2023 retrieved a total of 1855 records, which were uploaded into the Rayyan platform. After deduplication, 1544 records underwent manual title and abstract screening, of which 57 were identified as potentially eligible and underwent full text review. We excluded 46 reports [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ] (see Additional file 2 : Table S2), leaving 11 eligible studies which were included in our systematic review [ 17 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. Search rerun on September 15th, 2023 retrieved additional 130 deduplicated records, none of which was selected for full text review. Also, no additional eligible study was identified from reference lists of relevant papers.

Study characteristics

Table 1 displays the characteristics of the 11 included studies. Overall 3803 patients (range from 40 to 819 per study) were involved.

All studies were single-center with observational design, five of which with retrospective data collection [ 17 , 83 , 84 , 86 , 87 ]. The vast majority was conducted in Asia (eight in China [ 17 , 80 , 82 , 83 , 84 , 86 , 87 , 88 ], two in India [ 79 , 85 ]), and only one in Europe [ 81 ].

The target condition was generically indicated as bacterial infection in six studies [ 17 , 79 , 85 , 86 , 87 , 88 ], whereas five studies focused exclusively on pulmonary infection [ 80 , 81 , 82 , 83 , 84 ]. The reference standards used to define infection varied. Three studies applied Centers for Disease Control (CDC) criteria [ 81 , 83 , 86 ], and the others all used positive cultures, either alone [ 17 , 79 , 85 ], or in combination with different parameters including cultures, imaging, laboratory findings, and clinical signs [ 80 , 82 , 84 , 87 , 88 ] (Table  1 ). Only one study did not report the technique adopted for measuring plasmatic PCT [ 88 ], while all other studies used the chemiluminescence immunoassay. However, only five studies provided information on the specific assay and its sensitivity range [ 79 , 80 , 81 , 84 , 87 ].

Timing of PCT measurement also varied, with four studies performing only one measurement, three studies on the first PostOperative Day (POD) [ 83 , 84 , 85 ], and the other at ICU admission [ 88 ]. The longest reported monitoring period was POD 5 in four studies [ 17 , 80 , 82 , 86 ].

Risk of bias assessment

The methodological quality assessments with the QUADAS-2 tool results are summarized in Fig.  2 and further illustrated for individual studies in Fig.  3 .

figure 2

Risk of bias and applicability concerns graph: review authors' judgments about each domain presented as percentages across included studies

figure 3

Risk of bias and applicability concerns summary: review authors' judgments about each domain for each included study

No study had a low risk of bias in all 4 domains. For the domain of risk of bias in patient selection, only five studies provided clear definitions of exclusion criteria and were judged as ‘low’ risk. Regarding the risk of bias for index tests, none of the studies prespecified a threshold and therefore they were all rated as ‘high risk’. Only one of the studies was judged to be at high risk of bias for the reference standard domain and for the patient flow and timing domain [ 79 ]. Seven studies were rated as ‘low’. Only three studies [ 79 , 86 , 88 ] were considered to have concerns about applicability, all in terms of patient selection. Further details on how judgments were made for each individual study are provided in Additional file 3 : Table S3.

In the light of the issues that emerged from the risk of bias assessment, ten of the eleven studies were included in the meta-analysis. The study by Chakravarthy et al. [ 79 ] was excluded, because it exhibited high risk of bias in three domains and because it did not specify the execution time of the index test, making it impossible to attribute the outcome to a specific postoperative day.

Overall accuracy of PCT

Figure  4 shows the diagnostic accuracy of PCT in detecting bacterial infection after cardiac surgery, as reported in each of the 10 studies (2984 patients) included in the meta-analysis. The forest plots highlight the heterogeneity in test timing and in thresholds reported by each study, and in the corresponding values of Se and Sp and their 95%CI. The two diamonds represent, respectively, the pooled estimation of Se (0.70, 95%CI 0.67–0.73) and Sp (0.76, 95%CI 0.71–0.81). Concerning heterogeneity, through univariate analysis independent by thresholds, we determined values of I 2  = 15.5 and Q  = 28.4, which do not highlight significant heterogeneity ( p  = 0.243).

figure 4

Forest plot of PCT diagnostic accuracy

Concerning other diagnostic accuracy values, pooled median PLR, NLR and DOR of PCT were 2.96 (95%CI 2.33–3.74), 0.40 (95%CI 0.35–0.46), and 7.53 (95%CI 5.18–10.60), respectively. Based on the meaning attributed to the PLR value, a diseased patient is nearly three times more likely to have a positive test compared to a non-diseased patient; conversely, considering NLR, a non-diseased patient is 2.5 times more likely to have a negative test compared to a diseased patient. Furthermore, the value of DOR indicated that for PCT the odds for positivity among subjects with bacterial infection were nearly eight times higher than the odds for positivity among subjects without bacterial infections.

Results of bivariate model analysis using multiple thresholds within a study are depicted in Fig.  5 .

figure 5

Summary receiver operating characteristic (SROC) curve (bivariate analysis using multiple thresholds within a study) for diagnostic test accuracy. Each color identifies a different study for individual POD

The first two scatterplots from the top (panel A and B) show the optimal threshold as 3 ng/mL (with corresponding Se 0.67 (95%CI 0.47–0.82) and Sp 0.73 (95%CI 0.65–0.79)), which allows to best identify the diseased and non-diseased groups (solid and dashed lines) in terms of probability positive test and in terms of the corresponding maximum value of the Youden index.

The two lower scatterplots (panel C and D) display the individual ROC curves for each study and the SROC curve corresponding to the optimal threshold. The AUC of the SROC is of 0.75 (IC95% 0.29–0.95), which is considered to be “good” diagnostic accuracy [ 89 ] even though wide variability was observed.

Table 2 reports performance measures, calculated considering prespecified ranges of thresholds and prevalence. Predictive values are further illustrated by continuous lines in Fig.  6 , in which the threshold range is amplified (up to 20). As evident in Panel A, PPV varies approximately between 0.50 and 0.70, when prevalence is high (30%). Regarding NPV, the value is always > 90% when prevalence is < 20% (regardless of the threshold), and is reduced to 83% when prevalence is high (30%).

figure 6

Plots illustrating corresponding A positive predictive values and B negative predictive values for different PCT threshold and prevalences, based on the multiple thresholds model

The results of the analysis where the interaction term threshold*day was included are displayed in Additional file 4 : Table S4. The corresponding coefficient value is equal to − 0.24 (95%CI − 0.48 to 0.00), implying that the threshold should be decreased by 0.24 points per day. Although this finding is close to statistical significance ( p  = 0.053), for explorative purposes we examined it for each of the 6 PODs (Fig.  7 ). Starting from POD 1 to POD 4, the FN rate is reduced as the threshold decreases. This is especially true on POD 2, for which the finding is statistically significant ( p  = 0.019) (see Additional file 5 : Table S5), identifying it as the probable best time point to use PCT for the diagnosis of infection.

figure 7

Interaction plot for different thresholds and for each POD. The lines represent diseased and non-diseased groups. The X axis reports unit increment/decrement of the threshold coefficient. variations

Infection after cardiac surgery is a common complication but its timely diagnosis is challenging, since surgery, especially with the use of CPB, is a well-known trigger of systemic inflammation, producing biochemical and clinical patterns very similar to the ones observed during infection[ 5 ]. As a consequence, many markers of infection were shown to be unreliable in this condition [ 90 ].

Main findings

To our knowledge, this is the first systematic review and meta-analysis investigating the role of PCT for the diagnosis of postoperative infection only including adult patients after cardiac surgery. Our meta-analysis, including 10 studies and 2984 patients, assessed the diagnostic test accuracy of PCT, considering different thresholds and different time points reported in included studies. Bivariate analysis using multiple thresholds within a study enabled us to highlight important characteristics of the diagnostic test. Specifically, we identified the optimal threshold value at 3 ng/mL, which is considerably higher than the 0.5 to 1.0 ng/mL range generally recommended for the diagnosis of postoperative infection[ 8 ]. However, even when considering this optimal threshold, test performance was limited, with a sensitivity of 67% and specificity of 73%. These findings may be due to the presence of systemic inflammation immediately after surgery, a hypothesis also supported by our analysis of the interaction between threshold and POD, which suggested that the threshold should be reduced daily to improve PCT diagnostic accuracy, and especially to increase the positive predictive value. Our analysis also suggested that POD 2 may be the best timing to diagnose infection with PCT, an indication also reported by other studies [ 82 , 91 ]. Another interesting aspect worth noting, particularly relevant for clinical practice, is the test’s considerable ability to identify non-diseased individuals (NPV between 83 and 98%, with a prevalence range between 30 and 5%), and its poor utility in identifying diseased patients (PPV never exceeding 60%, even considering a high prevalence of 30%). This suggests that the use of procalcitonin in this context is useful to exclude, and not to confirm, the presence of a bacterial infection.

Concerning risk of bias assessment, various problems were detected. One of the main issue concerned the fact that threshold determination occurred a posteriori by ROC curve analysis in all studies, which may have led to optimistic test performance. Moreover, none of the studies was multicenter and none formally defined sample size a priori considering study endpoints.

Comparison of our results with other meta-analyses was not possible, because the only one published recently on this topic [ 16 ] considered both adults and children, and the analysis model used did not take into account the different thresholds reported in individual studies.

Strengths and limitations

This systematic review was conducted following rigorous methodology, for search strategy development, evidence analysis and quality appraisal, involving a multiprofessional research team. One of the main strengths of this work lies in the advanced meta-analysis methods used to summarize data according to multiple threshold values in each study. Furthermore, the use of strict eligibility criteria for our review (clear definition of target condition diagnosis, only adult populations and only publications from the last 10 years) helped reduce heterogeneity, thus improving generalizability of results. In particular, the decision to apply a date restriction was due to the fact that perioperative standards of care (e.g. surgical techniques, extracorporeal circulation, Intensive Care Unit (ICU) care, etc.) have improved considerably in the last decade, leading to a reduction of surgery-related stress, and thus of SIRS, which may be misclassified as infection [ 92 , 93 , 94 ]. Although minimally invasive cardiac surgery, miniaturized and biocompatible CPB circuits, and fast-track protocols were all introduced over 20 years ago, their implementation has accelerated over the past decade [ 94 , 95 , 96 , 97 ].We also decided to exclude patients with transcatheter interventions, as these procedures are associated with a significantly lower degree of systemic inflammation, are usually performed on older, sicker patients, and could therefore impact on the generalizability of the results to the cardiac surgical population [ 98 , 99 , 100 ].

Some limitations of this work should also be acknowledged. Firstly, we only included studies that clearly indicated the diagnostic criteria applied to confirm infection, which may have lead to exclude relevant studies that did not report this aspect accurately. Unfortunately, we could not verify this potential bias with funnel plots, since this is not feasible in meta-analyses of diagnostic studies with multiple thresholds. Furthermore, the decision to apply a date restriction might have led to the exclusion of relevant studies. Secondly, included studies used different reference standards, which may have affected reliability of results. Furthermore, we acknowledge that although the analyzed literature aimed to exclude patients with preoperative infection, cases of undiagnosed preoperative infection cannot be ruled out, and this may have influenced results. Thirdly, in all studies, even when PCT measurements were taken on different days, the number of patients at risk considered for measuring test accuracy remained constant. This may have influenced the determination of the optimal threshold. Moreover, this prevented an unbiased estimation of the threshold for each POD. Finally, all included studies are observational, five of which with retrospective data collection, including one case–control study. This may have influenced reliability of results.

This meta-analysis shows that in this target population, PCT performance is moderate, and accuracy good but not strong. Furthermore, the high NPV and low PPV values suggest the need for a paradigm shift in the use of PCT as a diagnostic marker for infection after cardiac surgery. In fact, while PCT is usually measured to confirm a suspected infection or as a screening tool in high risk populations, our results specific to individuals who underwent cardiac surgery suggest that for these patients it could rather be used to help exclude an infection that is deemed improbable. Another practical finding of this work is that a post-cardiac surgical PCT cutoff higher than that routinely employed in other aspects of clinical practice should be used. However, the optimal threshold of 3 ng/mL and time point of POD2 obtained in this meta-analysis need to be further investigated in large, well-designed randomized trials, aiming to establish whether health outcomes of patients receiving the test are better than those of patients who do not, corresponding to Phase IV diagnostic studies in the classification of Sackett and Haynes [ 101 ]. Only if robust evidence emerge, will it be possible to provide indications for clinical practice.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Area Under the Curve

Confidence Interval

Cardiopulmonary Bypass

Diagnostic Odds Ratio

False Negative

False Positive

Intensive Care Unit

Negative Likelihood Ratio

Procalcitonin

Positive Likelihood Ratio

Postoperative Day

Sensitivity

Systemic Inflammatory Response Syndrome

Specificity

Summary Receiver Operating Characteristic

True Negative

True Positive

Abukhodair A, Alqarni MS, Alzahrani A, Bukhari ZM, Kadi A, Baabbad FM, et al. Risk factors for postoperative infections in cardiac surgery patients: a retrospective study. Cureus. 2023;15(8): e43614. https://doi.org/10.7759/cureus.43614 .

Article   PubMed   PubMed Central   Google Scholar  

Alghamdi BA, Alharthi RA, AlShaikh BA, Alosaimi MA, Alghamdi AY, Yusnoraini N, et al. Risk factors for post-cardiac surgery infections. Cureus. 2022;14(11): e31198. https://doi.org/10.7759/cureus.31198 .

Denny KJ, De Waele J, Laupland KB, Harris PNA, Lipman J. When not to start antibiotics: avoiding antibiotic overuse in the intensive care unit. Clin Microbiol Infect. 2020;26(1):35–40. https://doi.org/10.1016/j.cmi.2019.07.007 .

Article   CAS   PubMed   Google Scholar  

Heffernan AJ, Denny KJ. Host diagnostic biomarkers of infection in the ICU: where are we and where are we going? Curr Infect Dis Rep. 2021;23(4):4. https://doi.org/10.1007/s11908-021-00747-0 .

Papp M, Kiss N, Baka M, Trásy D, Zubek L, Fehérvári P, et al. Procalcitonin-guided antibiotic therapy may shorten length of treatment and may improve survival-a systematic review and meta-analysis. Crit Care. 2023;27(1):394. https://doi.org/10.1186/s13054-023-04677-2 .

Kraft F, Schmidt C, Van Aken H, Zarbock A. Inflammatory response and extracorporeal circulation. Best Pract Res Clin Anaesthesiol. 2015;29(2):113–23. https://doi.org/10.1016/j.bpa.2015.03.001 .

Article   PubMed   Google Scholar  

Sager R, Kutz A, Mueller B, Schuetz P. Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med. 2017;15(1):15. https://doi.org/10.1186/s12916-017-0795-7 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cleland DA, Eranki AP. Procalcitonin. 2023. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing.

Heilmann E, Gregoriano C, Schuetz P. Biomarkers of infection: are they useful in the ICU? Semin Respir Crit Care Med. 2019;40(4):465–75. https://doi.org/10.1055/s-0039-1696689 .

Layios N, Lambermont B, Canivet JL, Morimont P, Preiser JC, Garweg C, et al. Procalcitonin usefulness for the initiation of antibiotic treatment in intensive care unit patients. Crit Care Med. 2012;40(8):2304–9. https://doi.org/10.1097/CCM.0b013e318251517a .

Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181–247. https://doi.org/10.1007/s00134-021-06506-y .

Jerome E, McPhail MJ, Menon K. Diagnostic accuracy of procalcitonin and interleukin-6 for postoperative infection in major gastrointestinal surgery: a systematic review and meta-analysis. Ann R Coll Surg Engl. 2022;104(8):561–70. https://doi.org/10.1308/rcsann.2022.0053 .

Jerome E, Cavazza A, Menon K, McPhail MJ. Systematic review and meta-analysis of the diagnostic accuracy of procalcitonin for post-operative sepsis/infection in liver transplantation. Transpl Immunol. 2022;74: 101675. https://doi.org/10.1016/j.trim.2022.101675 .

Cousin F, Ortega-Deballon P, Bourredjem A, Doussot A, Giaccaglia V, Fournel I. Diagnostic accuracy of procalcitonin and C-reactive protein for the early diagnosis of intra-abdominal infection after elective colorectal surgery: a meta-analysis. Ann Surg. 2016;264(2):252–6. https://doi.org/10.1097/SLA.0000000000001545 .

Yu XY, Wang Y, Zhong H, Dou QL, Song YL, Wen H. Diagnostic value of serum procalcitonin in solid organ transplant recipients: a systematic review and meta-analysis. Transplant Proc. 2014;46(1):26–32. https://doi.org/10.1016/j.transproceed.2013.07.074 .

Li Q, Zheng S, Zhou PY, Xiao Z, Wang R, Li J. The diagnostic accuracy of procalcitonin in infectious patients after cardiac surgery: a systematic review and meta-analysis. J Cardiovasc Med (Hagerstown). 2021;22(4):305–12. https://doi.org/10.2459/JCM.0000000000001017 .

Miao Q, Chen SN, Zhang HJ, Huang S, Zhang JL, Cai B, et al. A pilot assessment on the role of procalcitonin dynamic monitoring in the early diagnosis of infection post cardiac surgery. Front Cardiovasc Med. 2022;2(9): 834714. https://doi.org/10.3389/fcvm.2022.834714 .

Article   Google Scholar  

International prospective register of systematic reviews (PROSPERO). https://www.crd.york.ac.uk/prospero/ (accessed on 3 November, 2023)

Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ. 2021;29(372): n160. https://doi.org/10.1136/bmj.n160 .

Salameh JP, Bossuyt PM, McGrath TA, Thombs BD, Hyde CJ, Macaskill P, et al. Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist. BMJ. 2020;14(370): m2632. https://doi.org/10.1136/bmj.m2632 .

Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36(5):309–32. https://doi.org/10.1016/j.ajic.2008.03.002 .

Rayyan–Intelligent Systematic Review. https://www.rayyan.ai/ (accessed on 3 November, 2023).

Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, QUADAS-2 Group, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009 .

QUADAS-2 background document. University of Bristol. https://www.bristol.ac.uk › background-doc (Accessed on 18 October, 2023)

Bürkner PC, Doebler P. Testing for publication bias in diagnostic meta-analysis: a simulation study. Stat Med. 2014;33(18):3061–77. https://doi.org/10.1002/sim.6177 .

Article   MathSciNet   PubMed   Google Scholar  

Shim SR. Meta-analysis of diagnostic test accuracy studies with multiple thresholds for data integration. Epidemiol Health. 2022;44: e2022083. https://doi.org/10.4178/epih.e2022083 .

Deeks JJ, Altman DG. Diagnostic tests 4: likelihood ratios. BMJ. 2004;329(7458):168–9. https://doi.org/10.1136/bmj.329.7458.168 .

Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56(11):1129–35. https://doi.org/10.1016/s0895-4356(03)00177-x .

Steinhauser S, Schumacher M, Rücker G. Modelling multiple thresholds in meta-analysis of diagnostic test accuracy studies. BMC Med Res Methodol. 2016;16(1):97. https://doi.org/10.1186/s12874-016-0196-1 .

Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. BMJ. 1994;309(6965):1351–5. https://doi.org/10.1136/bmj.309.6965.1351 .

Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. https://doi.org/10.1136/bmj.327.7414.557 .

Melsen WG, Bootsma MC, Rovers MM, Bonten MJ. The effects of clinical and statistical heterogeneity on the predictive values of results from meta-analyses. Clin Microbiol Infect. 2014;20(2):123–9. https://doi.org/10.1111/1469-0691.12494 .

Amouzeshi A, Abedi F, Zardast M, Rezaeian Bilondi Y, Amouzeshi Z. Prognostic value of procalcitonin for morbidity and mortality in patients after cardiac surgery. Cardiol Res Pract. 2021;26(2021):1542551. https://doi.org/10.1155/2021/1542551 .

Bauer A, Hausmann H, Schaarschmidt J, Scharpenberg M, Troitzsch D, Johansen P, et al. Shed-blood-separation and cell-saver: an integral Part of MiECC? Shed-blood-separation and its influence on the perioperative inflammatory response during coronary revascularization with minimal invasive extracorporeal circulation systems—a randomized controlled trial. Perfusion. 2018;33(2):136–47. https://doi.org/10.1177/0267659117728195 .

Baumbach H, Rustenbach CJ, Ahad S, Nagib R, Albert M, Ratge D, et al. Minimally invasive extracorporeal bypass in minimally invasive heart valve operations: a prospective randomized trial. Ann Thorac Surg. 2016;102(1):93–100. https://doi.org/10.1016/j.athoracsur.2016.01.043 .

Baysal A, Dogukan M, Toman H. Is procalcitonin a valuable marker for identification of postoperative complications after coronary artery bypass graft surgery with cardiopulmonary bypass? Crit care. 2015;19(Suppl 1):272. https://doi.org/10.1186/cc14352 .

Boeken U, Mehdiani A, Albert A, Aubin H, Dalyanoglu H, Westenfeld R, et al. Early detection of imminent morbidity after heart transplantation (htx) by means of procalcitonin (PCT) combined with highly sensitive cardiac troponin T (hs-cTNT). J Heart Lung Transplant. 2019;38(4):S291. https://doi.org/10.1016/j.healun.2019.01.729 .

Brocca A, Virzì GM, de Cal M, Giavarina D, Carta M, Ronco C. Elevated levels of procalcitonin and interleukin-6 are linked with postoperative complications in cardiac surgery. Scand J Surg. 2017;106(4):318–24. https://doi.org/10.1177/1457496916683096 .

Brodska H, Valenta J, Pelinkova K, Stach Z, Sachl R, Balik M, et al. Diagnostic and prognostic value of presepsin vs. established biomarkers in critically ill patients with sepsis or systemic inflammatory response syndrome. Clin Chem Lab Med. 2018;56(4):658–68. https://doi.org/10.1515/cclm-2017-0839 .

Cheng ZB, Chen H. Higher incidence of acute respiratory distress syndrome in cardiac surgical patients with elevated serum procalcitonin concentration: a prospective cohort study. Eur J Med Res. 2020;25(1):11. https://doi.org/10.1186/s40001-020-00409-2 .

Clementi A, Brocca A, Virzì GM, de Cal M, Giavarina D, Carta M, et al. Procalcitonin and interleukin-6 levels: are they useful biomarkers in cardiac surgery patients? Blood Purif. 2017;43(4):290–7. https://doi.org/10.1159/000454672 .

Clementi A, Virzì GM, Muciño-Bermejo MJ, Nalesso F, Giavarina D, Carta M, et al. Presepsin and procalcitonin levels as markers of adverse postoperative complications and mortality in cardiac surgery patients. Blood Purif. 2019;47(1–3):140–8. https://doi.org/10.1159/000494207 .

Cui J, Gao M, Huang H, Huang X, Zeng Q. Dexmedetomidine improves lung function by promoting inflammation resolution in patients undergoing totally thoracoscopic cardiac surgery. Oxid Med Cell Longev. 2020;7(2020):8638301. https://doi.org/10.1155/2020/8638301 .

Article   CAS   Google Scholar  

Diab M, Tasar R, Sponholz C, Bauer M, Lehmann T, Faerber G, et al. Can preoperative measurement of mid-regional proadrenomedullin predict postoperative organ dysfunction and mortality in patients undergoing valvular surgery? Thorac Cardiovasc Surg. 2019;67(S01):S1–100. https://doi.org/10.1055/s-0039-1678808 .

Dreymueller D, Goetzenich A, Emontzpohl C, Soppert J, Ludwig A, Stoppe C. The perioperative time course and clinical significance of the chemokine CXCL16 in patients undergoing cardiac surgery. J Cell Mol Med. 2016;20(1):104–15. https://doi.org/10.1111/jcmm.12708 .

Franeková J, Sečník P Jr, Lavríková P, Kubíček Z, Hošková L, Kieslichová E, et al. Serial measurement of presepsin, procalcitonin, and C-reactive protein in the early postoperative period and the response to antithymocyte globulin administration after heart transplantation. Clin Transplant. 2017. https://doi.org/10.1111/ctr.12870 .

Hanafy DA, Harta IKAP, Prasetya IMI, Busroh PW, Soetisna TW, Sugisman, et al. Effectivity of dexamethasone in patients undergoing off-pump coronary artery bypass surgery. Asian Cardiovasc Thorac Ann. 2021;29(5):388–93. https://doi.org/10.1177/0218492320977648 .

Heredia-Rodríguez M, Bustamante-Munguira J, Fierro I, Lorenzo M, Jorge-Monjas P, Gómez-Sánchez E, et al. Procalcitonin cannot be used as a biomarker of infection in heart surgery patients with acute kidney injury. J Crit Care. 2016;33:233–9. https://doi.org/10.1016/j.jcrc.2016.01.015 .

Heredia-Rodríguez M, Bustamante-Munguira J, Lorenzo M, Gómez-Sánchez E, Álvarez FJ, Fierro I, et al. Procalcitonin and white blood cells, combined predictors of infection in cardiac surgery patients. J Surg Res. 2017;15(212):187–94. https://doi.org/10.1016/j.jss.2017.01.021 .

Hrazdilova O, Wagner R, Pavlak P, Spaailova H. Intensive care infection score: a novel infection marker in perioperative medicine. Anesth Analg. 2021;133(3):316.

Google Scholar  

Imperiali CE, Lopez-Delgado JC, Dastis-Arias M, Sanchez-Navarro L. Biomaker evaluation for major adverse cardiovascular event development in patients undergoing cardiac Surgery. Adv Lab Med. 2020;1(4):20200031. https://doi.org/10.1515/almed-2020-0031 .

Jiao J, Wang M, Zhang J, Shen K, Liao X, Zhou X. Procalcitonin as a diagnostic marker of ventilator-associated pneumonia in cardiac surgery patients. Exp Ther Med. 2015;9(3):1051–7. https://doi.org/10.3892/etm.2015.2175 .

Kettner J, Holek M, Franekova J, Jabor A, Pindak M, Riha H, et al. Procalcitonin dynamics after long-term ventricular assist device implantation. Heart Lung Circ. 2017;26(6):599–603. https://doi.org/10.1016/j.hlc.2016.09.014 .

Klingele M, Bomberg H, Poppleton A, Minko P, Speer T, Schäfers HJ, et al. Elevated procalcitonin in patients after cardiac surgery: a hint to nonocclusive mesenteric ischemia. Ann Thorac Surg. 2015;99(4):1306–12. https://doi.org/10.1016/j.athoracsur.2014.10.064 .

Klingele M, Bomberg H, Schuster S, Schäfers HJ, Groesdonk HV. Prognostic value of procalcitonin in patients after elective cardiac surgery: a prospective cohort study. Ann Intensive Care. 2016;6(1):116. https://doi.org/10.1186/s13613-016-0215-8 .

Kupiec A, Adamik B, Kozera N, Gozdzik W. Elevated procalcitonin as a risk factor for postoperative delirium in the elderly after cardiac surgery: a prospective observational study. J Clin Med. 2020;9(12):3837. https://doi.org/10.3390/jcm9123837 .

Lagrost L, Girard C, Grosjean S, Masson D, Deckert V, Gautier T, et al. Low preoperative cholesterol level is a risk factor of sepsis and poor clinical outcome in patients undergoing cardiac surgery with cardiopulmonary bypass. Crit Care Med. 2014;42(5):1065–73. https://doi.org/10.1097/CCM.0000000000000165 .

Laudisio A, Nenna A, Musarò M, Angeletti S, Nappi F, Lusini M, et al. Perioperative management after elective cardiac surgery: the predictive value of procalcitonin for infective and noninfective complications. Future Cardiol. 2021;17(8):1349–58. https://doi.org/10.2217/fca-2020-0245 .

Liu H, Luo Z, Liu L, Yang XM, Zhuang YM, Zhang Y, et al. Early kinetics of procalcitonin in predicting surgical outcomes in type A aortic dissection patients. Chin Med J (Engl). 2017;130(10):1175–81. https://doi.org/10.4103/0366-6999.205857 .

Ma B, He L, Xia Y, Chi L, Piao Z, Sun X, et al. The value of serum amyloid a on early diagnosing and prognosis for perioperative patients with extracorporeal circulation. Indian J Pharm Sci. 2020;82:26–30. https://doi.org/10.36468/pharmaceutical-sciences.spl.29 .

Mitaka C, Dong Z, Haraguchi G. The value of serum procalcitonin level for differentiation of infectious from noninfectious systemic inflammatory response syndrome after cardiac surgery. Intensive Care Med. 2013;39:S350.

Mlejnsky F, Klein AA, Lindner J, Maruna P, Kvasnicka J, Kvasnicka T, et al. A randomised controlled trial of roller versus centrifugal cardiopulmonary bypass pumps in patients undergoing pulmonary endarterectomy. Perfusion. 2015;30(7):520–8. https://doi.org/10.1177/0267659114553283 .

Mohamed HE, Ibrahim MM, Ali MS. Procalcitonin as an early predictor of systemic inflammatory response with or without infection in patients undergoing coronary artery bypass surgery. Anesth Analg. 2016;123:20–1. https://doi.org/10.1213/01.ane.0000492421.04036.20 .

Mony U, Sanju S, Jain P, Sugavanan K, Sebastian A, Theertha M, et al. Detection of dysregulated host response by flow cytometry may pre-empt early diagnosis of sepsis after cardiac surgery. Blood. 2019. https://doi.org/10.1182/blood-2019-129782 .

Nadziakiewicz P, Grochla M, Krauchuk A, Pióro A, Szyguła-Jurkiewicz B, Baca A, et al. Procalcitonin kinetics after heart transplantation and as a marker of infection in early postoperative course. Transplant Proc. 2020;52(7):2087–90. https://doi.org/10.1016/j.transproceed.2020.02.117 .

Nemeth E, Kovacs E, Racz K, Soltesz A, Szigeti S, Kiss N, et al. Impact of intraoperative cytokine adsorption on outcome of patients undergoing orthotopic heart transplantation-an observational study. Clin Transplant. 2018;32(4): e13211. https://doi.org/10.1111/ctr.13211 .

Pan T, Jiang CY, Zhang H, Han XK, Zhang HT, Jiang XY, et al. The low-dose colchicine in patients after non-CABG cardiac surgery: a randomized controlled trial. Crit Care. 2023;27(1):49. https://doi.org/10.1186/s13054-023-04341-9 .

Partylova EA, Petrishchev YI, Kudryavtsev IV, Malkova OG, Levit AL. Immunity and its effect on the incidence of multiple organ failure in patients after the heart surgery. Obshchaya Reanimatol. 2019;15(4):32–41. https://doi.org/10.15360/1813-9779-2019-4-32-41 .

Pavalascu A, Arce Arias T, Jimenez Lizarazu ML, Nuñez Martinez JM, Tebar Boti E. Value of procalcitonin (PCT) as diagnostic test of infection in cardiac surgery (CS). Intensive Care Med Exp. 2015;3(Suppl 1):A107. https://doi.org/10.1186/2197-425X-3-S1-A107 .

Article   PubMed Central   Google Scholar  

Perrotti A, Chenevier-Gobeaux C, Ecarnot F, Barrucand B, Lassalle P, Dorigo E, et al. Relevance of endothelial cell-specific molecule 1 (Endocan) plasma levels for predicting pulmonary infection after cardiac surgery in chronic kidney disease patients: the endolung pilot study. Cardiorenal Med. 2017;8(1):1–8. https://doi.org/10.1159/000479337 .

Perrotti A, Chenevier-Gobeaux C, Ecarnot F, Bardonnet K, Barrucand B, Flicoteaux G, et al. Is endocan a diagnostic marker for pneumonia after cardiac surgery? The ENDOLUNG study. Ann Thorac Surg. 2018;105(2):535–41. https://doi.org/10.1016/j.athoracsur.2017.07.031 .

Popov D, Plyushch M, Ovseenko S, Abramyan M, Podshchekoldina O, Yaroustovsky M. Prognostic value of sCD14-ST (presepsin) in cardiac surgery. Kardiochir Torakochirurgia Pol. 2015;12(1):30–6. https://doi.org/10.5114/kitp.2015.50565 .

Saito J, Hashiba E, Mikami A, Kudo T, Niwa H, Hirota K. Pilot study of changes in presepsin concentrations compared with changes in procalcitonin and c-reactive protein concentrations after cardiovascular surgery. J Cardiothorac Vasc Anesth. 2017;31(4):1262–7. https://doi.org/10.1053/j.jvca.2017.02.007 .

Schmidt T, Pargger H, Seeberger E, Eckhart F, von Felten S, Haberthür C. Effect of high-dose sodium selenite in cardiac surgery patients: a randomized controlled bi-center trial. Clin Nutr. 2018;37(4):1172–80. https://doi.org/10.1016/j.clnu.2017.04.019 .

Schoe A, Schippers EF, Struck J, Ebmeyer S, Klautz RJ, de Jonge E, et al. Postoperative pro-adrenomedullin levels predict mortality in thoracic surgery patients: comparison with Acute Physiology and Chronic Health Evaluation IV Score. Crit Care Med. 2015;43(2):373–81. https://doi.org/10.1097/CCM.0000000000000709 .

Song YY, Zhang B, Gu JW, Zhang YJ, Wang Y. The predictive value of procalcitonin in ventilator-associated pneumonia after cardiac valve replacement. Scand J Clin Lab Investig. 2020;80(5):423–6. https://doi.org/10.1080/00365513.2020.1762242 .

Xie M, Chen YT, Zhang H, Zhang HT, Pan K, Chen XF, et al. Diagnostic value of procalcitonin and interleukin-6 on early postoperative pneumonia after adult cardiac surgery: a prospective observational study. Heart Surg Forum. 2022;25(1):E020–9. https://doi.org/10.1532/hsf.4297 .

Zhao D, Zhou J, Haraguchi G, Arai H, Mitaka C. Procalcitonin for the differential diagnosis of infectious and non-infectious systemic inflammatory response syndrome after cardiac surgery. J Intensive Care. 2014;3(2):35. https://doi.org/10.1186/2052-0492-2-35 .

Chakravarthy M, Kavaraganahalli D, Pargaonkar S, Hosur R, Harivelam C, Bharadwaj A, et al. Elevated postoperative serum procalcitonin is not indicative of bacterial infection in cardiac surgical patients. Ann Card Anaesth. 2015;18(2):210–4. https://doi.org/10.4103/0971-9784.154480 .

Chen W, Zhong K, Guan Y, Zhang HT, Zhang H, Pan T, et al. Evaluation of the significance of interleukin-6 in the diagnosis of postoperative pneumonia: a prospective study. BMC Cardiovasc Disord. 2022;22(1):306. https://doi.org/10.1186/s12872-022-02744-0 .

de la Varga-Martínez O, Martín-Fernández M, Heredia-Rodríguez M, Ceballos F, Cubero-Gallego H, Priede-Vimbela JM, et al. Influence of renal dysfunction on the differential behaviour of procalcitonin for the diagnosis of postoperative infection in cardiac surgery. J Clin Med. 2022;11(24):7274. https://doi.org/10.3390/jcm11247274 .

Jin H, Gu SP, Wang Y, Pan K, Chen Z, Cao HL, et al. Diagnosis value of procalcitonin variation on early pneumonia after adult cardiac surgery. Heart Surg Forum. 2021;24(4):E734–40. https://doi.org/10.1532/hsf.3987 .

Li Y, Zhang J, He Z. Early predictive value of procalcitonin for the diagnosis of pulmonary infections after off-pump coronary artery bypass grafting. Heart Surg Forum. 2021;24(1):E004–8. https://doi.org/10.1532/hsf.3381 .

Liu J, Zhang W, Wang Q, Li Z, Lv M, Shi C, et al. The early diagnostic value of procalcitonin in pneumonia after off-pump coronary artery bypass grafting surgery. Med Sci Monit. 2019;26(25):3077–89. https://doi.org/10.12659/MSM.913704 .

Sharma P, Patel K, Baria K, Lakhia K, Malhotra A, Shah K, et al. Procalcitonin level for prediction of postoperative infection in cardiac surgery. Asian Cardiovasc Thorac Ann. 2016;24(4):344–9. https://doi.org/10.1177/0218492316640953 .

Wang H, Cui N, Niu F, Xu H, Long Y, Liu D. Usefulness of procalcitonin for the diagnosis of infection in cardiac surgical patients. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2017;29(10):897–901. https://doi.org/10.3760/cma.j.issn.2095-4352.2017.10.007 .

Zhu Y, Cao X, Zhang K, Xie W, Xu D, Zhong C. Delta mean neutrophil volume (ΔMNV) is comparable to procalcitonin for predicting postsurgical bacterial infection. J Clin Lab Anal. 2014;28(4):301–5. https://doi.org/10.1002/jcla.21684 .

Zou L, Song X, Hong L, Shen X, Sun J, Zhang C, et al. Intestinal fatty acid-binding protein as a predictor of prognosis in postoperative cardiac surgery patients. Medicine (Baltimore). 2018;97(33): e11782. https://doi.org/10.1097/MD.0000000000011782 .

Šimundić AM. Measures of diagnostic accuracy: basic definitions. EJIFCC. 2009;19(4):203–11.

PubMed   PubMed Central   Google Scholar  

Jukic T, Ihan A, Stubljar D. Dynamics of inflammation biomarkers C-reactive protein, leukocytes, neutrophils, and CD64 on neutrophils before and after major surgical procedures to recognize potential postoperative infection. Scand J Clin Lab Invest. 2015;75(6):500–7. https://doi.org/10.3109/00365513.2015.1057759 .

Aouifi A, Piriou V, Blanc P, Bouvier H, Bastien O, Chiari P, et al. Effect of cardiopulmonary bypass on serum procalcitonin and C-reactive protein concentrations. Br J Anaesth. 1999;83(4):602–7. https://doi.org/10.1093/bja/83.4.602 .

Permanyer E, Munoz-Guijosa C, Padró JM, Ginel A, Montiel J, Sánchez-Quesada JL, et al. Mini-extracorporeal circulation surgery produces less inflammation than off-pump coronary surgery. Eur J Cardiothorac Surg. 2020;57(3):496–503. https://doi.org/10.1093/ejcts/ezz291 .

Naruka V, Salmasi MY, Arjomandi Rad A, Marczin N, Lazopoulos G, Moscarelli M, et al. Use of cytokine filters during cardiopulmonary bypass: systematic review and meta-analysis. Heart Lung Circ. 2022;31(11):1493–503. https://doi.org/10.1016/j.hlc.2022.07.015 .

Anastasiadis K, Antonitsis P, Murkin J, Serrick C, Gunaydin S, El-Essawi A, et al. 2021 MiECTiS focused update on the 2016 position paper for the use of minimal invasive extracorporeal circulation in cardiac surgery. Perfusion. 2023;38(7):1360–83. https://doi.org/10.1177/02676591221119002 .

Abad C, Urso S, Clavo B. New trends in cardiac surgery: toward a less-invasive surgical procedure. J Thorac Cardiovasc Surg. 2019;157(5):e268–9. https://doi.org/10.1016/j.jtcvs.2018.12.003 .

Ilcheva L, Risteski P, Tudorache I, Häussler A, Papadopoulos N, Odavic D, et al. Beyond conventional operations: embracing the era of contemporary minimally invasive cardiac surgery. J Clin Med. 2023;12(23):7210. https://doi.org/10.3390/jcm12237210 .

Paparella D, Fattouch K, Moscarelli M, Santarpino G, Nasso G, Guida P, et al. Current trends in mitral valve surgery: a multicenter national comparison between full-sternotomy and minimally-invasive approach. Int J Cardiol. 2020;1(306):147–51. https://doi.org/10.1016/j.ijcard.2019.11.137 .

Lindman BR, Goldstein JS, Nassif ME, Zajarias A, Novak E, Tibrewala A, et al. Systemic inflammatory response syndrome after transcatheter or surgical aortic valve replacement. Heart. 2015;101(7):537–45. https://doi.org/10.1136/heartjnl-2014-307057 .

Uhle F, Castrup C, Necaev AM, Grieshaber P, Lichtenstern C, Weigand MA, et al. Inflammation and its consequences after surgical versus transcatheter aortic valve replacement. Artif Organs. 2018;42(2):E1–12. https://doi.org/10.1111/aor.13051 .

Werner N, Zahn R, Beckmann A, Bauer T, Bleiziffer S, Hamm CW, GARY Executive Board, et al. Patients at intermediate surgical risk undergoing isolated interventional or surgical aortic valve implantation for severe symptomatic aortic valve stenosis. Circulation. 2018;138(23):2611–23. https://doi.org/10.1161/CIRCULATIONAHA.117.033048 .

Sackett DL, Haynes RB. The architecture of diagnostic research. BMJ. 2002;324(7336):539–41. https://doi.org/10.1136/bmj.324.7336.539 .

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Sandra Rossi and Caterina Caminiti have contributed equally to this work.

Authors and Affiliations

Department of Anesthesia and Intensive Care Medicine, University Hospital of Parma, Parma, Italy

Davide Nicolotti, Silvia Grossi, Valeria Palermo, Federico Pontone & Sandra Rossi

Clinical and Epidemiological Research Unit, University Hospital of Parma, Parma, Italy

Giuseppe Maglietta, Francesca Diodati, Matteo Puntoni & Caterina Caminiti

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DN and SR conceived the study. DN, CC, FD and GM designed the protocol. FD defined the search strategies. DN and VP performed title and abstract screening. SG and FP performed full text review. CC acted as third reviewer. CC and FD conducted risk of bias analysis and MP acted as third reviewer. CC and GM extracted data and MP resolved conflicts. GM, MP and CC defined the statistical analysis plan. GM performed all statistical analyses. All authors contributed to data interpretation. CC, FD and GM drafted the manuscript. All authors revised the manuscript critically and approved the final version.

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Correspondence to Giuseppe Maglietta .

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Supplementary Information

Additional file 1:.

Search strategy.

Additional file 2:

Studies excluded after full text review and corresponding reasons.

Additional file 3:

Review authors’ judgements about each risk of bias item for each included study.

Additional file 4:

Results Linear Mixed-Effects Models with interaction terms of Threshold × Group × POD.

Additional file 5:

Results Linear Mixed-Effects Models with interaction terms of Threshold × Group × POD (as factor).

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Nicolotti, D., Grossi, S., Palermo, V. et al. Procalcitonin for the diagnosis of postoperative bacterial infection after adult cardiac surgery: a systematic review and meta-analysis. Crit Care 28 , 44 (2024). https://doi.org/10.1186/s13054-024-04824-3

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Efficacy and safety of the combination of camrelizumab and apatinib in the treatment of liver cancer: a systematic review and single-arm meta-analysis

  • Min Chen 1 ,
  • Yanglei Li 1 &
  • Minyu Cheng 1  

BMC Gastroenterology volume  24 , Article number:  55 ( 2024 ) Cite this article

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To evaluate the efficacy and safety of the combination of camrelizumab and apatinib in the treatment of liver cancer and to furnish clinical recommendations for pharmacological interventions.

PubMed, Embase, Web of Science and the Cochrane Library were scrutinized for research publications from their inception to 22 December 2023. Bibliographic perusal and data procurement were executed. The quality of the included studies was evaluated employing the MINORS tool. Meta-analysis was conducted utilizing Stata 15.0 software.

A total of 10 studies involving 849 patients were included in the meta-analysis. The study revealed that the objective response rate (ORR) of the combined therapy was 28% (95% CI: 23%-34%), the disease control rate (DCR) was 69% (95% CI: 64%-73%), the median progression-free survival (mPFS) was 5.87 months (95% CI: 4.96–6.78), the median overall survival (mOS) was 19.35 months (95% CI: 17.53–21.17), the incidence of any grade adverse events was 90% (95% CI: 85%-95%), and the occurrence of grade 3 or higher adverse events was 49% (95% CI: 27%-71%).

The combination of camrelizumab and apatinib exhibits commendable effectiveness in the management of liver cancer; nevertheless, vigilance should be exercised concerning potential adverse reactions in clinical applications to enhance the safety of pharmacological interventions.

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Introduction

Liver cancer constitutes one of the most prevalent malignant neoplasms globally, ranking 6th in incidence amidst all cancers and 3rd in fatalities, exhibiting the most accelerated escalation in mortality throughout the past several decades [ 1 , 2 , 3 ]. Hepatocellular carcinoma accounts for the highest proportion of liver cancer cases, ranging from 75 to 85% [ 4 , 5 ]. The onset of liver cancer is often latent, and the preponderance of patients have already advanced to intermediate or progressive stages at the time of initial detection, thereby losing the prospects for surgical intervention and localized therapy. Systemic pharmacological intervention typically constitutes the sole recourse, and the swift advancement of immune checkpoint inhibitors (ICIs) has introduced novel therapeutic alternatives and engendered optimism for patients afflicted with intermediate and advanced liver cancer [ 6 , 7 , 8 ]. Nonetheless, studies [ 9 , 10 ] have ascertained that the impact of ICI monotherapy on hepatic neoplasms is less than optimal, thereby inciting inquiries into concomitant therapy with molecularly targeted pharmaceuticals. Targeted agents reconfigure the neoplastic immune microenvironment, efficaciously amplifying the potency of immunotherapy and yielding a synergistic outcome [ 11 , 12 ]. The initial clinical ramifications of the conjunction of the immune checkpoint inhibitor camrelizumab and the antiangiogenic inhibitor apatinib have manifested as auspicious, and this strategy has surfaced as a novel trajectory in the therapeutic landscape of hepatic malignancies. Camrelizumab, a programmed cell death receptor-1 (PD-1) inhibitor, operates by impeding the interplay between PD-1 and its cognate ligand, programmed cell death ligand-1 (PD-L1), subsequently interrupting the immunosuppressive pathway exploited by malignant entities. This revitalizes the immunological response, reestablishes immune surveillance capabilities, and generates sustained anti-neoplastic effects. At present, camrelizumab has exhibited propitious results in the clinical handling of classical Hodgkin's lymphoma, hepatocellular carcinoma, pulmonary neoplasms, and esophageal squamous cell carcinoma [ 13 , 14 ]. Apatinib, a vascular endothelial growth factor receptor-2 (VEGFR-2) antagonist, functions by impeding the phosphorylation of VEGFR-2, thereby attenuating downstream signalling cascades and curbing tumour angiogenesis to exert its anti-neoplastic properties. This agent has demonstrated promising therapeutic outcomes in advanced gastric adenocarcinoma, gastroesophageal junction adenocarcinoma, and hepatocellular carcinoma [ 15 , 16 ]. Presently, emerging clinical investigations suggest that the combination therapy of camrelizumab and apatinib may offer certain advantages in the clinical management of liver cancer [ 15 , 16 ]. However, the precise therapeutic efficacy and safety profile of this regimen remain to be conclusively established [ 17 ]. Consequently, this study conducted a comprehensive systematic review and meta-analysis to evaluate the efficacy and safety of camrelizumab in conjunction with apatinib for the treatment of liver cancer, with the aim of providing evidence-based guidance for clinical practice.

Materials and methodology

Literature search.

The search encompassed databases such as PubMed, Embase, Web of Science, the Cochrane Library, and ClinicalTrials.gov, spanning from their inception to 22 December 2023. Search terms incorporated "Hepatocellular Carcinomas", "Liver Cancer", "Liver Cell Carcinoma", "camrelizumab", "SHR-1210", "apatinib", "rivoceranib" and "YN-968D1", utilizing both MeSH terms and free-text queries.

We have applied for the PROSPERO registration (CRD42023442948).

Inclusion and exclusion criteria

Inclusion criteria.

(1) Eligible patients were aged 18 years or older with histopathologically or cytologically confirmed hepatocellular carcinoma or radiologically assessed by enhanced computed tomography or magnetic resonance imaging combined with detection of serum tumour markers; (2) The intervention under investigation is the combined treatment of camrelizumab and apatinib; (3) Studies must report efficacy endpoints and adverse events, encompassing an objective response rate (ORR), disease control rate (DCR), median overall survival (mOS), median progression-free survival (mPFS),adverse events (AEs) and grade 3 or higher adverse events (AEs); (4) Study designs comprise randomised control trials, non-randomised control trials and single-arm studies, etc.

Exclusion criteria

(1) Animal and in vitro experiments, basic research; (2) Conference abstracts, reviews, commentaries, case reports; (3) Aggregate reporting of results from multiple populations or disease cohorts; (4) Duplicate publications; (5) Literature from which valid outcome data cannot be extracted.

Data extraction

Two investigators independently assessed the titles and abstracts of identified publications, performing full-text analysis on eligible articles to determine their final inclusion. Disagreements were resolved through discussions involving a third reviewer. Key information extracted from the original studies encompassed: (1) basic information about the included studies, such as author details, publication dates, and study design; (2) fundamental characteristics of study participants, including total sample size and age and gender distribution of enrolled cases; (3) specific intervention approaches and follow-up durations; (4) pertinent outcome measures; (5) information required for literature quality appraisal.

Literature quality assessment

Given that the most included studies were single-arm trials, the Methodological Index for Non-Randomized Studies (MINORS) assessment criteria were employed for literature quality evaluation [ 18 ]. The assessment entailed 12 indicators, with the first eight (I-VIII) pertaining to single-arm studies without a control group. The numbers I-VIII in the assessment criteria mean: I, a clearly stated objective; II, inclusion of consecutive patients; III, prospective data collection; IV, endpoints appropriate to the objective of the study; V, unbiased assessment of the study endpoint; VI, follow-up period appropriate to the study objective; VII, loss to follow-up less than 5%;VIII, prospective calculation of study size. Each indicator was scored on a scale of 0–2 points: 0 points denoted non-reporting, 1 point signified reported but with insufficient information and 2 points indicated reported with adequate information, and a very objective assessment of each indicator was made. A final score of 13–16 points indicated high-quality studies, and 9–12 points denoted medium-quality studies. According to the MINORS appraisal instrument, this meta-analysis incorporated solely literature of intermediate to high quality.

Statistical analysis methods

Stata 15.0 software was utilized to perform the statistical analysis of the extracted data. The odds ratio (OR) was used for dichotomous variables and the mean difference (MD) was used as the combined effect statistic for continuous variables. The effect size of all pooled results was reported as a 95% confidence interval (CI) with upper and lower limits. The heterogeneity of the included studies was assessed using I 2 and Cochran's Q test. A fixed-effects model was implemented for analysis when I 2  ≤ 50% and P ≥ 0.1. In contrast, when I 2  > 50% and P  < 0.1, indicating significant study heterogeneity, a random-effects model was adopted for analysis. The sensitivity analysis was performed for the pooled results with high heterogeneity. In addition, meta-regression is used to further explore the sources of heterogeneity. The collective findings were visually depicted using forest plots. The potential publication bias was scrutinized utilizing Egger's test, with a P  > 0.05, suggesting an absence of significant publication bias.

Retrieval results and fundamental characteristics of the literature

The search in collective databases yielded a total of 293 pertinent articles. After rigorous screening based on the inclusion and exclusion criteria, 92 duplicates were removed. Furthermore, based on a thorough assessment of their titles and abstracts, an additional 177 irrelevant articles were discarded. Ultimately, ten articles [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]were deemed eligible for analysis following a meticulous examination of their full text. The detailed flowchart outlining the literature screening process is demonstrated in Fig.  1 , while the essential characteristics of the selected articles are comprehensively depicted in Table  1 .

figure 1

PRISMA flow diagram of the study process. PRISMA, Preferred Reporting Items for Systematic review and Meta-analysis

The most included articles were single-arm studies; thus, the MINORS criteria were employed for quality assessment. The results of the quality evaluation are presented in Table  2 .

Therapeutic efficacy indicators

Objective response rate (orr).

In total, 10 publications were incorporated, encompassing 849 patients, with 239 individuals attaining objective disease remission. The aggregated analysis indicated that the ORR of camrelizumab combined with apatinib for liver cancer was 28% (95% CI: 23%-34%, I 2  = 56.9%, p  = 0.013), as depicted in Fig.  2 A. As I 2  = 56.9% > 50%, the random effects model was selected for the analysis, and the sensitivity analysis was continued to test the source of heterogeneity, the results of the sensitivity analysis showed good stability of the study, as shown in Fig.  2 B. Moreover, a supplementary subgroup analysis of the objective remission by first-line and second-line therapies was performed. The findings revealed that the ORR for camrelizumab combined with apatinib as a first-line intervention was 30% (95% CI: 25%-36%, I 2  = 37.7%, p  = 0.170), while for second-line therapy, it was 22% (95% CI: 13%-30%, I 2  = 43.3%, p  = 0.133), as illustrated in Fig.  2 C, suggesting that this combined strategy exhibits a higher objective remission rate when employed as first-line treatment for liver cancer.

figure 2

A Forest plot delineating ORR of camrelizumab in combination with apatinib for liver cancer treatment; B Sensitivity analysis on ORR of camrelizumab in combination with apatinib for liver cancer treatment; C Forest plot of subgroup analysis on ORR of camrelizumab in combination with apatinib as first-line or second-line therapy for liver cancer

Disease Control Rate (DCR)

Altogether, 6 articles comprising 7 research groups were included (Zhiming Zeng (2021) was subdivided into first-line and second-line treatment cohorts), totalling 345 patients, with 234 individuals exhibiting controlled disease progression. The aggregated analysis revealed that the DCR of camrelizumab combined with apatinib for liver cancer treatment was 69% (95% CI: 64%-73%, I 2  = 30.5%, p  = 0.195), as depicted in Fig.  3 A.

figure 3

A Forest plot illustrating DCR of camrelizumab combined with apatinib for liver cancer treatment; B Forest plot delineating mPFS of camrelizumab in combination with apatinib for liver cancer treatment; C Sensitivity analysis on mPFS of camrelizumab in combination with apatinib for liver cancer treatment; D Forest plot illustrating mOS of camrelizumab combined with apatinib for liver cancer treatment

Median Progression-Free Survival (mPFS)

In total, 6 articles encompassing 7 research groups were incorporated (Jianming Xu (2021) was categorized into first-line and second-line treatment cohorts), and the aggregated analysis indicated that the mPFS of camrelizumab combined with apatinib for liver cancer treatment was 5.87 months (95% CI: 4.96–6.78, I 2  = 73.1%, p  = 0.001), as illustrated in Fig.  3 B. As I 2  = 73.1%, we selected the random effects model for the analysis and continued the sensitivity analysis to test the source of heterogeneity. The results of the sensitivity analysis showed good stability of the analysis, as shown in Fig.  3 C.

Median Overall Survival (mOS)

Three studies were incorporated into the statistical analysis, and the results demonstrated that the mOS of camrelizumab combined with apatinib for liver cancer treatment was 19.35 months (95% CI: 17.53–21.17, I 2  = 49.7%, p  = 0.137), as depicted in Fig.  3 D.

A comprehensive analysis of the adverse reaction incidence rates for liver cancer treatment utilizing camrelizumab combined with apatinib was conducted, encompassing 6 articles and a total of 674 patients. Among them, 622 patients encountered general adverse reactions, with an occurrence rate of 90% (95% CI: 85%-95%, I 2  = 92.8%, p  = 0.000); 455 patients experienced grade 3 or higher adverse reactions, with an incidence rate of 49% (95% CI: 27%-71%, I 2  = 97.7%, p  = 0.000), as delineated in Table  3 . Due to the high level of heterogeneity, we performed meta-regression analyses by study design, which showed any grade AEs ( p  = 0.939) and grade 3 or higher AEs ( p  = 0.229), as delineated in Table  4 , indicating that study design covariate was not significantly associated with PFS and OS and other factors may be at play. Predominantly, the general adverse reactions with a higher incidence encompass Thrombocytopenia (51%, 95% CI: 41%-62%, I 2  = 71.3%, p  = 0.031), Hypertension (45%, 95% CI: 27%-62%, I 2  = 95.6%, p  = 0.000), and Hand-foot skin reaction (45%, 95% CI: 33%-57%, I 2  = 84.1%, p  = 0.000), in addition to Leukopenia (40%), Proteinuria (37%), Abdominal pain (34%), Diarrhea (31%), Hepatotoxicity (24%), Fever (20%), Hypothyroidism (20%), RCCEP (19%), Rash (18%), Fatigue (17%), and Nausea and vomiting (11%), as delineated in Table  3 . Primarily, severe adverse reactions with a higher incidence include Hypertension (19%, 95% CI: 4%-34%, I 2  = 98.5%, p  = 0.000), Thrombocytopenia (9%, 95% CI: 1%-17%, I 2  = 88.7%, p  = 0.000), and Hand-foot skin reaction (6%, 95% CI: 3%-9%, I 2  = 32.4%, p  = 0.218), along with Proteinuria (5%), Hepatotoxicity (3%), Abdominal pain (2%), Diarrhea (2%), and Rash (1%), as portrayed in Table  3 .

Publication bias analysis

An analysis of publication bias was executed on the incorporated studies utilizing the Egger test. The results revealed that ORR ( p  = 0.268), DCR ( p  = 0.068), mPFS ( p  = 0.469), mOS ( p  = 0.828), incidence of general adverse reactions ( p  = 0.420), and incidence of ≥ 3-grade adverse reactions ( p  = 0.250) conformed to the criterion of p  > 0.05. This implies that no significant publication bias exists within the study.

All of the above results are summarised in Table  5 .

In recent years, the burgeoning development of immunosuppressive agents has ushered liver cancer treatment into a new epoch of immunotherapy [ 29 ]. Particularly, the collaborative strategy with antivascular targeted therapy has demonstrated promising application potential in liver cancer clinical treatment, offering new therapeutic hopes for patients afflicted with liver cancer [ 30 , 31 , 32 , 33 ]. Research indicates that the synergy between immunotherapy and anti-vascular targeted therapy yields an augmented antitumor effect [ 34 , 35 ]. Anti-angiogenic drugs can facilitate the infiltration and activation of immune cells within tumours, mediate the upregulation of IFNγ, enhance the expression of PD-1 and PD-L1, boost the sensitivity of immunotherapy within tumours, alter the M1/M2 ratio of tumour-associated macrophages, diminish the infiltration of regulatory T cells and monocytes in tissues, restructure the tumour immune microenvironment, and effectively elevate the efficacy of immunotherapy [ 11 , 12 ]. Furthermore, immunosuppressive agents may trigger the recruitment of immune subpopulations possessing vascular regulatory activity, potentially serving as a target for anti-angiogenic treatment [ 36 ]. Consequently, the combined utilization of both can ameliorate the local vascular microenvironment, effectively eradicate tumour cells, and jointly enhance clinical treatment outcomes. Among these, the PD-1 inhibitor camrelizumab combined with the VEGFR-2 inhibitor apatinib has exhibited favourable treatment prospects in the clinical management of liver cancer.

Through a comprehensive analysis of the ten incorporated articles in this study, it was discovered that the clinical efficacy of camrelizumab combined with apatinib in treating liver cancer is commendable. This combination not only yields satisfactory objective remission rates and disease control rates but also provides patients with significant benefits in terms of median progression-free survival and median overall survival. Moreover, through an in-depth subgroup analysis, it was discerned that the objective remission rate of the combined regimen as first-line therapy was considerably higher compared to second-line therapy. On the one hand, this could be attributed to the diminished functional status of patients when their second-line treatment was adopted, which results in a lower tolerable drug dosage, thereby directly reducing the therapeutic effect of the combined regimen. On the other hand, prior treatment may render the immune microenvironment within the patient’s body increasingly complex. For instance, if first-line treatment has already involved immunosuppressive agents, it might lead to the development of drug-resistant antibodies, engendering resistance to immunotherapy [ 37 ] and subsequently weakening the therapeutic effect of second-line medications. Nonetheless, due to the limited number of studies presently incorporated in the analysis, the research findings necessitate more clinical trial data and a larger sample size for validation and substantiation.

The results of CARES-310, a global phase 3 randomized open-label trial on camrelizumab plus apatinib indicate that this combination therapy presents a promising first-line treatment option for unresectable HCC with a positive benefit-to-risk profile. This study is the first to report significant benefits in progression-free survival and overall survival with the combination of an anti-PD-1 antibody and an oral small molecule anti-angiogenic agent as first-line treatment for unresectable HCC, compared to sorafenib. However, it is important to acknowledge the limitations of this study, including its open-label design and the fact that the majority of participants were from Asia and had hepatocellular carcinoma of viral aetiology. Further research is required to confirm the effectiveness of the treatment in other patient subgroups [ 27 ]. Although camrelizumab in combination with apatinib in the treatment of HCC has achieved a high probability of being the most effective treatment in terms of both OS and PFS, more direct comparative research analysis with existing standard first-line treatments is needed in the future [ 38 , 39 ]. Furthermore, in the analysis of adverse reactions, it was ascertained that the overall safety profile of the combined regimen is generally acceptable. However, it may also provoke the risk of adverse reactions in patients, such as thrombocytopenia, hypertension, and hand-foot skin reaction. Meanwhile, immune-related adverse events such as liver damage also deserve clinical attention. Immune-related liver injury during treatment with ICI is relatively common in patients with HCC and is often detected by elevated ALT/AST levels [ 40 ]. Therefore, it is crucial to closely monitor relevant clinical symptoms and indicators through a more comprehensive clinical examination, such as cardiovascular function tests, routine blood tests and liver function tests, etc., so that the physician can adjust the dosage of the drug and undertake appropriate interventions to ensure clinical efficacy and safety. In addition, multidisciplinary management, which requires integrated collaboration between the specialties involved in the management of patients with HCC, is valuable. For example, treatment for HCC patients is administered after multidisciplinary assessment and according to the practice of participating institution, and continued until disease progression or unacceptable toxicity. Toxicity management, including dose modification, is performed in accordance with the summary of product characteristics for the agents [ 40 ]. The development of multidisciplinary care for HCC patients is essential to optimise the management of treatment side effects and improve patient outcomes.While this study endeavoured to incorporate as many pertinent studies as possible that fulfil the criteria, it still exhibits the following limitations: 1. Variations in disease subtypes, medication dosage, sample size, follow-up duration, and statistical methods contribute to increased research heterogeneity; 2. It is unable to acquire comprehensive data for additional subgroup analyses; 3. Some included studies have a brief follow-up period, not attaining overall survival (OS) and progression-free survival (PFS); 4. The most included studies are single-arm trials with smaller sample sizes, necessitating larger-scale, multicentre, randomized controlled clinical trials for combined analysis and evaluation, with the aim of providing more objective and efficacious evidence-based medicine for clinical treatment.

In conclusion, camrelizumab in combination with apatinib shows a favourable therapeutic effect and a manageable safety profile in HCC. Further investigation can delve into the effective biomarkers of this combined regimen, identify the optimal treatment population, and thus achieve precision and individualization in liver cancer therapy. Simultaneously, it may be worthwhile to explore combining this treatment with surgical procedures, radiofrequency ablation, and interventional therapies to afford liver cancer patients a broader array of treatment options and opportunities, thereby harnessing its clinical potential and value.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.

Article   PubMed   Google Scholar  

Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49.

Llovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, Lencioni R, Koike K, Zucman-Rossi J, Finn RS. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6.

Singal AG, Lampertico P, Nahon P. Epidemiology and surveillance for hepatocellular carcinoma: New trends. J Hepatol. 2020;72(2):250–61.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Llovet JM, Castet F, Heikenwalder M, Maini MK, Mazzaferro V, Pinato DJ, Pikarsky E, Zhu AX, Finn RS. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol. 2022;19(3):151–72.

Article   CAS   PubMed   Google Scholar  

Pinter M, Jain RK, Duda DG. The current landscape of immune checkpoint blockade in hepatocellular carcinoma: a review. JAMA Oncol. 2021;7(1):113–23.

Article   PubMed   PubMed Central   Google Scholar  

Sangro B, Sarobe P, Hervás-Stubbs S, Melero I. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2021;18(8):525–43.

Finn RS, Ryoo BY, Merle P, Kudo M, Bouattour M, Lim HY, Breder V, Edeline J, Chao Y, Ogasawara S, et al. Pembrolizumab As Second-Line Therapy in Patients With Advanced Hepatocellular Carcinoma in KEYNOTE-240: A Randomized, Double-Blind, Phase III Trial. J Clin Oncol. 2020;38(3):193–202.

Yau T, Park JW, Finn RS, Cheng AL, Mathurin P, Edeline J, Kudo M, Han KH, Harding JJ, Merle P, et al. CheckMate 459: A randomized, multi-center phase III study of nivolumab (NIVO) vs sorafenib (SOR) as first-line (1L) treatment in patients (pts) with advanced hepatocellular carcinoma (aHCC). Ann Oncol. 2019;30:874-+.

Article   Google Scholar  

Lee WS, Yang H, Chon HJ, Kim C. Combination of anti-angiogenic therapy and immune checkpoint blockade normalizes vascular-immune crosstalk to potentiate cancer immunity. Exp Mol Med. 2020;52(9):1475–85.

Shigeta K, Datta M, Hato T, Kitahara S, Chen IX, Matsui A, Kikuchi H, Mamessier E, Aoki S, Ramjiawan RR, et al. Dual programmed death receptor-1 and vascular endothelial growth factor receptor-2 blockade promotes vascular normalization and enhances antitumor immune responses in hepatocellular carcinoma. Hepatology (Baltimore, MD). 2020;71(4):1247–61.

Markham A, Keam SJ. Camrelizumab: first global approval. Drugs. 2019;79(12):1355–61.

Mo H, Huang J, Xu J, Chen X, Wu D, Qu D, Wang X, Lan B, Wang X, Xu J, et al. Safety, anti-tumour activity, and pharmacokinetics of fixed-dose SHR-1210, an anti-PD-1 antibody in advanced solid tumours: a dose-escalation, phase 1 study. Br J Cancer. 2018;119(5):538–45.

Scott LJ. Apatinib: A Review in Advanced Gastric Cancer and Other Advanced Cancers. Drugs. 2018;78(7):747–58.

Zhao D, Hou H, Zhang X. Progress in the treatment of solid tumors with apatinib: a systematic review. Onco Targets Ther. 2018;11:4137–47.

Bai X, Chen Y, Zhang X, Zhang F, Liang X, Zhang C, Wang X, Lu B, Yu S, Liang T. CAPT: A multicenter randomized controlled trial of perioperative versus postoperative camrelizumab plus apatinib for resectable hepatocellular carcinoma. Ann Oncol. 2022;33(7):S868–S868.

Slim K, Nini E, Forestier D, Kwiatkowski F, Panis Y, Chipponi J. Methodological index for non-randomized studies (minors): development and validation of a new instrument. ANZ J Surg. 2003;73(9):712–6.

Xu JM, Zhang Y, Jia R, Yue CY, Chang LP, Liu RR, Zhang GR, Zhao CH, Zhang YY, Chen CX, et al. Anti-PD-1 Antibody SHR-1210 Combined with Apatinib for Advanced Hepatocellular Carcinoma, Gastric, or Esophagogastric Junction Cancer: An Open-label, Dose Escalation and Expansion Study. Clin Cancer Res. 2019;25(2):515–23.

Ju S, Zhou C, Yang C, Wang C, Liu J, Wang Y, Huang S, Li T, Chen Y, Bai Y, et al. Apatinib Plus Camrelizumab With/Without Chemoembolization for Hepatocellular Carcinoma: a real-world experience of a single center. Front Oncol. 2021;11:835889.

Xu J, Shen J, Gu S, Zhang Y, Wu L, Wu J, Shao G, Zhang Y, Xu L, Yin T, et al. Camrelizumab in Combination with Apatinib in Patients with Advanced Hepatocellular Carcinoma (RESCUE): A Nonrandomized, Open-label, Phase II Trial. Clin Cancer Res. 2021;27(4):1003–11.

Mei KM, Qin SK, Chen ZD, Liu Y, Wang LN, Zou JJ. Camrelizumab in combination with apatinib in second-line or above therapy for advanced primary liver cancer: cohort A report in a multicenter phase Ib/II trial. J Immunother Cancer. 2021;9(3):e002191.

Zeng Z, Jiang Y, Liu C, Zhu G, Ma F, Yang L, Qiu J, Tang J, Ye X, Peng T, et al. Efficacy and biomarker exploration of camrelizumab combined with apatinib in the treatment of advanced primary liver cancer: a retrospective study. Anticancer Drugs. 2021;32(10):1093–8.

Xia Y, Tang W, Qian X, Li X, Cheng F, Wang K, Zhang F, Zhang C, Li D, Song J, et al. Efficacy and safety of camrelizumab plus apatinib during the perioperative period in resectable hepatocellular carcinoma: a single-arm, open label, phase II clinical trial. J Immunother Cancer. 2022;10(4):e004656.

Yuan G, Li R, Li Q, Hu X, Ruan J, Fan W, Wang J, Huang W, Zang M, Chen J. Interaction between hepatitis B virus infection and the efficacy of camrelizumab in combination with apatinib therapy in patients with hepatocellular carcinoma: a multicenter retrospective cohort study. Ann Transl Med. 2021;9(18):1412.

Yuan GS, Cheng X, Li Q, Zang MY, Huang W, Fan WZ, Wu T, Ruan J, Dai WC, Yu WX, et al. Safety and Efficacy of Camrelizumab Combined with Apatinib for Advanced Hepatocellular Carcinoma with Portal Vein Tumor Thrombus: a multicenter retrospective study. Onco Targets Ther. 2020;13:12683–93.

Qin S, Chan SL, Gu S, Bai Y, Ren Z, Lin X, Chen Z, Jia W, Jin Y, Guo Y, et al. Camrelizumab plus rivoceranib versus sorafenib as first-line therapy for unresectable hepatocellular carcinoma (CARES-310): a randomised, open-label, international phase 3 study. Lancet. 2023;402(10408):1133–46.

Chen D, Chen X, Xu L, Wang Y, Zhu L, Kang M. Camrelizumab combined with apatinib in the treatment of patients with hepatocellular carcinoma: a real-world assessment. Neoplasma. 2023;70(4):580–7.

Dual Immunotherapy Makes Strides against HCC. Cancer Discov. 2022;12(4):OF1. https://doi.org/10.1158/2159-8290.CD-NB2022-0008 .

Finn RS, Qin S, Ikeda M, Galle PR, Ducreux M, Kim TY, Kudo M, Breder V, Merle P, Kaseb AO, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med. 2020;382(20):1894–905.

Galle PR, Finn RS, Qin S, Ikeda M, Zhu AX, Kim TY, Kudo M, Breder V, Merle P, Kaseb A, et al. Patient-reported outcomes with atezolizumab plus bevacizumab versus sorafenib in patients with unresectable hepatocellular carcinoma (IMbrave150): an open-label, randomised, phase 3 trial. Lancet Oncol. 2021;22(7):991–1001.

Pinato DJ, Fessas P, Cortellini A, Rimassa L. Combined PD-1/VEGFR Blockade: a New Era of treatment for hepatocellular cancer. Clin Cancer Res. 2021;27(4):908–10.

Yi M, Jiao DC, Qin S, Chu Q, Wu KM, Li AP. Synergistic effect of immune checkpoint blockade and anti-angiogenesis in cancer treatment. Mol Cancer. 2019;18:60.

Ciccarese C, Iacovelli R, Porta C, Procopio G, Bria E, Astore S, Cannella MA, Tortora G. Efficacy of VEGFR-TKIs plus immune checkpoint inhibitors in metastatic renal cell carcinoma patients with favorable IMDC prognosis. Cancer Treat Rev. 2021;100:102295.

Saeed A, Park R, Sun W. The integration of immune checkpoint inhibitors with VEGF targeted agents in advanced gastric and gastroesophageal adenocarcinoma: a review on the rationale and results of early phase trials. J Hematol Oncol. 2021;14(1):13.

Goedegebuure RSA, de Klerk LK, Bass AJ, Derks S, Thijssen V. Combining Radiotherapy with anti-angiogenic therapy and immunotherapy; a therapeutic triad for cancer? Front Immunol. 2018;9:3107.

Enrico D, Paci A, Chaput N, Karamouza E, Besse B. Antidrug Antibodies Against Immune Checkpoint Blockers: Impairment of Drug Efficacy or Indication of Immune activation? Clin Cancer Res. 2020;26(4):787–92.

Yang Q, Li G, Wu X, Lin H, Wu W, Xie X, Zhu Y, Cai W, Shi C, Zhuo S. A novel therapeutic strategy of combined camrelizumab and apatinib for the treatment of advanced hepatocellular carcinoma. Front Oncol. 2023;13:1136366.

Celsa C, Cabibbo G, Pinato D, Maria G, Enea M, Vaccaro M: Balancing efficacy and tolerability of first-line systemic therapies for advanced hepatocellular carcinoma: a network metanalysis. Liver Cancer. 2023. https://doi.org/10.1159/000531744 .

Celsa C, Cabibbo G, Fulgenzi CAM, Scheiner B, D’Alessio A, Manfredi GF, Nishida N, Ang C, Marron TU, Saeed A, et al. Characteristics and outcomes of immunotherapy-related liver injury in patients with hepatocellular carcinoma versus other advanced solid tumours. J Hepatol. 2023;S0168–8278(23):05272–8.

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Symptoms of posttraumatic stress, anxiety, and depression, along with their associated factors, among Eritrean refugees in Dabat town, northwest Ethiopia, 2023

  • Mihret Melese 1 ,
  • Wudneh Simegn 2 ,
  • Dereje Esubalew 3 ,
  • Liknaw Workie Limenh 4 ,
  • Wondim Ayenew 2 ,
  • Gashaw Sisay Chanie 5 ,
  • Abdulwase Mohammed Seid 5 ,
  • Alemante Tafese Beyna 6 ,
  • Melese Legesse Mitku 6 ,
  • Asefa Kebad Mengesha 6 &
  • Yibeltal Yismaw Gela 1  

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Refugee populations are forcibly displaced from their homes as a consequence of natural disasters and armed conflicts. Eritreans, initially displaced to the Maiayni camp within the Tigray region, have faced further relocation to Dabat town due to the conflict between the Tigray People Liberation Front (TPLF) and Ethiopian government forces. Subsequently, another conflict has arisen between the Amhara Popular Force (Fano) and Ethiopian government forces in Dabat town, disrupting its stability. These collective challenges in the new environment may contribute to the development of symptoms such as posttraumatic stress disorder (PTSD), anxiety, and depression. Currently, there is a lack of available data on these symptoms and their associated variables in Dabat Town. Thus, the objective of this study was to assess the prevalence of PTSD, anxiety, and depression symptoms, along with associated factors, among Eritrean refugees in Dabat town, northwest Ethiopia. This will provide significant evidence for developing and implementing mental health intervention strategies that specifically address the particular difficulties faced by refugees.

A community-based cross-sectional study was carried out from July 25 to September 30, 2023, in the Eritrean refugee camp in Dabat town. A systematic random sampling method was employed to select a total of 399 Eritrean refugees with 100 response rate. Data were collected using the standard validated Depression, Anxiety, and Stress Scale (DASS-21) questionnaire, which included socio-demographic characteristics. Summary statistics such as frequency and proportion were utilized to present the data in tables and figures. Binary logistic regression was employed to identify associated factors, and variables with a p -value ( p  ≤ 0.05) were considered statistically significant factors.

The findings of this study indicated that 45% (95% CI: 35.6-48.23), 33.6% (95% CI: 31.66–37.45), and 37.3% (95% CI: 35.56–40.34) of the participants had symptoms of depression, anxiety, and PTSD, respectively. Sex, age, employment status, lack of food or water, experience of torture or beating, and imprisonment emerged as statistically significant predictors of depression. Employment status, murder of family or friends, rape or sexual abuse, torture or beating, and lack of housing or shelter were statistically significantly associated with anxiety. PTSD was found to be significantly associated with sex, length of stay at the refugee camp, lack of housing, shelter, food, or water, experience of rape or sexual abuse, abduction, employment status, and murder of family or friends.

Conclusions and recommendation

The results of this study revealed that more than one-third of Eritreans living in the refugee camp in Dabat town had symptoms of PTSD, anxiety, and depression. This prevalence is higher than the previously reported studies. Various factors, including age, gender, monthly income, unemployment, experiences of rape or sexual abuse, witnessing the murder of family or friends, being torched or beaten, imprisonment, and deprivation of basic needs such as food, shelter, and water, were identified as contributors to the development of depression, anxiety, and PTSD. This research underscores the need for both governmental and non-governmental organizations to secure the provision of essential necessities such as food, clean water, shelter, clothing, and education. This study also suggested that Eritrean refugees be legally protected from rape, sexual abuse, arson, detention without cause, and kidnapping. Moreover, the study calls for health service providers to develop a mental health intervention plan and implement strategies to deliver mental health services at healthcare facilities for Eritrean refugees in the Dabat town Eritrean refugee camp.

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Introduction

Refugee populations are forcibly displaced from their homes as a consequence of natural disasters and armed conflicts [ 1 , 2 , 3 , 4 ]. A significant burden of psychiatric morbidity falls on populations exposed to the effects of war and forced migration, particularly those suffering from disorders like PTSD, anxiety, and depression [ 5 , 6 , 7 , 8 , 9 ]. Refugees face a higher likelihood of encountering acts of extreme violence, becoming targets of terrorist attacks, experiencing abduction and torture, enduring family separation, and undergoing forced migration. These circumstances can exacerbate symptoms related to stress, anxiety, and depression [ 10 , 11 , 12 , 13 ]. In 2023, data from the UN High Commissioner for Human Rights reported more than 18,000 civilian victims, with 7,031 fatalities and 11,327 injuries arising from the conflict between Russia and Ukraine [ 14 ]. The Russia-Ukraine war has resulted in a significant rise in the number of refugees leaving Ukraine for neighboring countries, accounting for approximately 2,871,519 of the deaths and injuries [ 15 ]. Furthermore, within the country, around 5,914,000 individuals have been internally displaced since November [ 16 ].

The prevalence of mental disorders among forcibly displaced populations shows variability, ranging from 2 to 88% for posttraumatic stress disorder (PTSD), 5 to 81% for depression, and 1 to 90% for generalized anxiety disorder [ 17 ]. In 2017, worldwide, anxiety disorders affected 260 million individuals, and 300 million people were affected by depression, leading to economic consequences totaling at least $1 trillion (USD) in annual lost productivity [ 18 ].

A meta-analysis carried out in 2009 on populations exposed to conflict and refugees indicated a depression prevalence of 30.8% [ 19 ]. In a similar systematic study carried out in 2019, 8,176 Syrian refugees who had been resettled reported having anxiety symptoms at a rate of 26% and 40% of depression [ 20 ]. In the Gaza Strip’s refugee camps, 23.9% of Palestinians were found to be suffering from PTSD [ 21 ]. The prevalence of depression, anxiety, and stress differs among various refugee camps in Africa. In Southern Sudan, the documented prevalence of depression was 49.9% [ 22 ]; while, in Uganda, the rates were 32% for Rwandan refugees and 48.1% for Somali refugees, respectively [ 23 ]. In the Maiayni refugee camp in the Tigray region of Ethiopia, the prevalence of depression among Eritreans was documented at 37.8% [ 24 ].

A number of factors, such as advanced age, unemployment, gender, a higher number of potentially traumatic experiences, low socioeconomic status, substance use disorders, the nature of the trauma, witnessing someone being killed or seriously injured, and socio-demographic characteristics like low socioeconomic status and marital status, can lead to the development of symptoms of depression, anxiety, and PTSD [ 14 , 25 , 26 , 27 , 28 , 29 , 30 ].

Eritreans, initially displaced to the Maiayni camp within the Tigray region, have faced further relocation to Dabat town due to the conflict between the Tigray People Liberation Front (TPLF) and Ethiopian government forces. Subsequently, another conflict has arisen between the Amhara Popular Force (Fano) and Ethiopian government forces in Dabat town, disrupting its stability.

Consequently, Eritreans in this area confront a variety of challenges, including shortages in food, healthcare, and education, as well as having a higher risk of experiencing problems like rape, sexual abuse, witnessing the murder of family or friends, imprisonment, torching, and abduction. These collective challenges in the new environment may contribute to the development of symptoms such as PTSD, anxiety, and depression. These collective challenges in the new environment may contribute to the development of symptoms such as PTSD, anxiety, and depression. Currently, there is a lack of available data on these symptoms and their predictor variables in Dabat Town. Thus, the objective of this study was to assess the prevalence of PTSD, anxiety, and depression symptoms, along with associated factors, among Eritrean refugees in Dabat town, northwest Ethiopia. This will provide significant evidence for developing and implementing mental health intervention strategies that specifically address the particular difficulties faced by Eritrean refugees. Moreover, the study provides valuable insights that can guide the delivery of humanitarian assistance for refugees.

Study area, design and period

A community-based cross-sectional study was conducted from July 25 to September 30, 2023, at the Eritrea refugee camp in Dabat town, which is located in the Amhara regional state in northwest Ethiopia. The Dabat district is located about 775 km from Addis Ababa, the capital city of Ethiopia, and approximately 75 km from Gondar town. The camp was established to accommodate nearly 30,000 Eritreans displaced from the Tigray region, specifically from the Maiayni refugee camp, due to the conflict between the TPLF and the Ethiopian Federal Government forces. There are 30,000 Eritreans populated in one camp in Dabat town, northwest Ethiopia.

Sample size determination and sampling technique

A total of 399 refugees were included in the study, based on an assumed proportion (p) of 37.8%, derived from a previous study conducted among Eritrean refugees at the Maiayni refugee camp [ 24 ].

With a 10% contingency rate, the final sample size was 399, Where; ni= initial sample size z α/2 = 1.96 (critical value for a normal distribution at a 95% confidence level), p = 0.46 (the proportion of stunting), d = 0.05 (the level of precision or acceptable error), Nf = final sample size.

A systematic random sampling method was employed to select a total of 193 male and 206 female Eritrean refugees.

Dependent variables

Depression, Anxiety, and Posttraumatic Stress (Yes/No).

Independent variables

Sociodemographic characteristics (gender, age, monthly income, educational background), behavioral factors (khat chewing, cigarette smoking, exercise, alcohol consumption), and experiences of rape or sexual abuse, abduction, murder, and the availability of basic necessities like food, water, clothes, and shelter. clinical and associated characteristics ( chronic diseases such as hypertension, Human immunodeficiency virus (HIV) and diabetes mellitus).

Operational definition

In this study, depression was operationalized through participants’ scores, where individuals scoring ≥ 10 out of a potential 63 points were categorized as experiencing depression, whereas those scoring 0–9 were classified as not manifesting symptoms of depression [ 31 ].

Anxiety in this study was determined by participants scoring more than 7 out of 63 points, while those without anxiety were identified as individuals scoring 0–7 points [ 31 ].

Posttraumatic stress disorder

It was specified that study participants with a score of 14 out of 63 were categorized as having PTSD, whereas participants without PTSD were identified as those scoring 0–14 points [ 31 ].

Data collection tool and procedure

Two skilled BSC nursing professionals collected the data using an interviewer-administered questionnaire. The questionnaire was translated from English into Tigrigna and back again by native speakers of both languages to ensure consistency and comprehensibility. The data collectors collected details on the educational background of the study participants by facilitating self-reporting through a questionnaire. The provided options in the questionnaire encompassed categories like “Never attended school,” “Primary School (Grade 1st–8th),” “Secondary School (Grade 9th–12th),” and “Diploma and above.” We used Lovibond’s short version of the DASS-21 (Depression, Anxiety, and Stress Scale-21), a psychological assessment tool designed to discern and distinguish symptoms associated with depression, anxiety, and stress [ 32 , 33 , 34 ]. Each of the three scales within the DASS-21 comprises seven items, which are further organized into subscales featuring similar content [ 32 , 33 , 34 ]. The threshold values for anxiety were categorized as follows: 0–7 (normal), 8–9 (mild), 10–14 (moderate), 15–19 (sever), and > 20 (extremely sever). The cut-off points for stress were delineated as 0–14 (normal), 15–18 (mild), 19–25 (moderate), 26–33 (sever), and > 34 (extremely sever ) [ 34 ].

Similarly for depression,0–9 (normal), mild [ 10 , 11 , 12 , 13 ], moderate [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ], sever [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ], ≥ 28 (extremely sever). The validity and reliability of the DAS-21 items have been tested and proven in previous studies conducted in Ethiopia [ 35 ].

Data management and statistical analysis

Epidata version 4.6 was utilized for data entry, and subsequent analysis was conducted using SPSS version 25. Summary statistics, including proportions and frequencies, were employed to present the results in tables and graphs. The binary logistic regression model was applied to identify associated factors of posttraumatic stress, anxiety, and depression. Variables associated with having p -values (≤ 0.2) in the bivariable logistic regression model were included in a multivariable logistic regression model. Statistical significance in the multivariable binary logistic regression was determined by a p -value of ( p  ≤ 0.05), with the adjusted odds ratio used to determine the strength of association. The normality of continuous data was assessed using the Shapiro-Wilk test, and the model’s fitness was evaluated through the Hosmer-Lemeshow goodness-of-fit test. The validity of the questionnaire was examined using Cronbach’s alpha, revealing a satisfactory reliability coefficient of 0.635.

Background characteristics of study participants

In this research, 399 participants were selected through a systematic random sampling technique, resulting in a 100% response rate. Approximately half of the participants were male, and the mean age of the study participants was 34 (± 0.54). Most of the research participants (66.4%) belonged to orthodox religious followers. Almost 50% of Eritrean refugees had jobs in the private sector. Of all the participants, 20.8% in the refugee camp have never attended formal education (Table  1 ).

Clinical and behavioral characteristics of study participants

In this study, it was reported that over 50% of the participants had engaged in Khat chewing. Almost 47% of participants were cigarette smokers, and about 47% of the participants were cigarette smokers. Among the participants, one-fourth suffered from mental illness. Over 40% of participants indicated that there was a shortage of food or water in the refugee camps. More than one-third of the participants reported instances of friends or family being murdered, while 45% of participants experienced torture or beatings in the camp (Table  2 ).

Prevalence of symptoms of depression, anxiety and PTSD

The results of this study revealed that 45% (95% CI: 35.6-48.23), 33.6% (95% CI: 31.66–37.45), and 37.3% (95% CI: 35.56–40.34) of the participants had symptoms of depression, anxiety, and PTSD, respectively (Table  3 ).

Factors associated with depression

In the bivariable analysis, variables such as sex, age, employment status, monthly income, education background, living conditions, incidents of rape or sexual abuse, lack of food or water, exposure to combat situations, the murder of family or friends, instances of being torched or beaten, abduction experiences, and imprisonment were considered as candidates for multivariable logistic regression ( p -value ≤ 0.2). However, in the multivariable logistic regression analysis at a 95% confidence interval, variables such as sex, age, employment status, lack of food or water, being torched or beaten, and imprisonment were found to be statistically significant predictors of depression. Females had a 1.23 times higher chance of having depression compared to males (AOR = 1.23; 95% CI: 1.09–34 ). Study participants aged 45 years and older had a higher chance of developing depression as compared to their counterparts (AOR = 3.53; 95% CI: 1.09–7.67). Participants without jobs were more depressed as compared to those with jobs (AOR = 1.22; 95% CI: 1.08–3.87). Individuals who reported not having enough food or water have a higher chance of developing depression than those who did not report it (AOR = 1.23; 95% CI: 1.07–3.22). Finally, participants with a history of previous imprisonment were more likely to develop depression compared to those without any history of incarceration (AOR = 1.45; 95% CI: 1.09–3.76) (Table  4 ).

Factors associated with anxiety

Employment status, murder of family or friends, being abducted, rape or sexual abuse, lack of food or water, being torched or beaten, and combat situation were considered as candidate variables of anxiety for multivariable analysis ( p  ≤ 0.2). Accordingly, employment status, murder of family or friends, rape or sexual abuse, being torched or beaten, and lack of housing or shelter were statistically significantly associated with anxiety at a p value of p  ≤ 0.05. Compared to participants with jobs, those without jobs had higher odds of anxiety (AOR = 2.42; 95% CI: 1.19–3.22). Individuals in the study who witnessed the murder of friends or family members were more likely to have anxiety than those who did not witness such a murder (AOR = 1.32; 95% CI: 1.16–1.63). Study participants who experienced sexual abuse or rape had a higher likelihood of anxiety than those who did not (AOR = 1.20; 95% CI: 1.04–4.37). Individuals who experienced being torched or beaten have a higher level of anxiety compared to those who did not undergo such traumatic events (AOR = 1.26; 95% CI: 1.09–3.21). Lastly, individuals with reports of homelessness or shelter deficiency had a higher level of anxiety compared to those who did not report (AOR = 1.24; 95% CI: 1.04–6.33) (Table  5 ).

Factors associated with PTSD

Variables like sex, age, employment status, murder of family or fringes, being abducted, rape or sexual abused, lack of food or water, Khat chewing, social support, length of stay at refugee camp, and lack of housing or shelter were candidate variables of PTSD for multivariable logistic regression ( p -value ≤ 0.2). In the final model, compared to men, women had a higher chance of developing stress (AOR = 1.20; 95% CI: 1.07–4.31). Abducted individuals had higher odds of developing PTSD compared to those who were not abducted. (AOR = 1.42; 95% CI: 1.09–4.77). Participants who had suffered sexual abuse or rape had a higher chance of developing PTSD compared to those who had not (AOR = 1.30; 95% CI: 1.17–3.66). Individuals who indicated not having enough food or water had a higher chance of developing PTSD compared to those who did not report (AOR = 1.23; 95% CI: 1.07–4.22). Lastly, refugees who resided in refugee camps for one year or more exhibited a higher likelihood of experiencing PTSD ( AOR = 2.63; 95% CI: 1.12–5.36) (Table  6 ).

The objective of this study was to assess the prevalence of PTSD, anxiety, and depression symptoms, along with associated factors, among Eritrean refugees in Dabat town, northwest Ethiopia. The research revealed that the symptoms of anxiety, depression, and posttraumatic stress disorder (PTSD) were identified as 33.6% (95% CI: 31.66–37.45), 45% (95% CI: 39.6-48.23), and 37.3% (95% CI: 35.56–40.34), respectively. This research was similar to a study conducted among individuals living in a refugee camp in Greece, indicating a prevalence of 35.3% for PTSD, 33.3% for depression, and 27.9% for anxiety [ 36 ]. Nevertheless, the prevalence of PTSD in this study was found to be lower compared to studies conducted among Syrian refugees in Germany, 75.3% [ 37 ]; Southern Sudan (49.9%) [ 22 ]; and Eritrean refugees in Maiayni camp, Ethiopia,37.8% [ 24 ]. However, the prevalence of anxiety in this research was higher compared to that among Syrian refugees in Germany, which was reported to be 14% [ 37 ]. This variation could be attributed to factors such as the intensity of conflict, legal constraints, severe treatment by authorities, socioeconomic challenges, language barriers, discrimination, social isolation, restricted access to health services, and a lack of access to essential resources like nutritious food, clean water, and adequate clothing [ 37 ].

In this research, depression showed a positive correlation with variables such as sex, age, employment status, inadequate access to food or water, experiences of torture or physical abuse, a lack of housing or shelter, and instances of imprisonment. Female Eritreans were more dispersed than males. These findings are consistent with supporting evidence from the Greece refugee camp study [ 37 ]. This could be explained by biological factors, such as fluctuations in ovarian hormone levels, especially estrogen, which can cause alterations in mood that lead to anxiety and depression in women [ 38 , 39 , 40 , 41 ]. Study participants aged 45 years and older demonstrated an increased likelihood of developing depression compared to their counterparts. This study was consistent with a study done in Iraq [ 42 ] and in Ethiopia, Eritrean refugee camp [ 24 ]. This could be attributed to the physiological changes in both the cardiovascular and neurological systems that occur during the aging process, potentially increasing susceptibility to depression [ 43 ]. Refugees without employment had a higher chance of developing the odds of depression compared to their counterparts. This study was supported by a study conducted in Mexico [ 44 ] and Afghanistan [ 45 ]. The state of unemployment results in an inability to fulfill basic needs and cover essential expenses; these challenges significantly contribute to the onset of depressive symptoms [ 46 , 47 ]. Refugees who reported a lack of food, water, shelter, and clothing in the refugee camp were found to have higher odds of experiencing depression compared to those who did not report such shortages. This study was similar to a study conducted in Uganda [ 48 ]. This could be due to insufficient food and water intake, which can lead to malnutrition and various health issues. Insufficient food and water intake can lead to malnutrition and various health issues. Malnutrition has been linked to changes in brain function and neurotransmitter imbalances, which can contribute to mood disorders, including depression [ 49 , 50 ]. This can lead to a sense of isolation, shame, and a diminished identity, all of which can contribute to the development of depression [ 51 ]. Eritrean refugees who experienced torture or imprisonment were more likely to have depression than those who did not. This study was similar to a study conducted at Nyarugusu Refugee Camp in Kigoma, Tanzania [ 52 ]. This might be attributed to the fact that physical abuse or torture can lead to lasting physical injuries with potential long-term consequences. The presence of chronic pain or disability can contribute to depression as individuals contend with both the physical and emotional ramifications of their experiences [ 53 ]. Furthermore, studies suggest that unavoidable or uncontrollable stressors, like torture, can result in decreased dopamine release in the nucleus accumbens, leading to impaired responsiveness to environmental stimuli. This, in turn, may play a role in the onset and exacerbation of depressive symptoms [ 54 ].

The results of the current study showed a significant association between anxiety and a number of variables, such as employment status, instances of rape or sexual abuse, the death of family members or friends, experiences of sexual abuse, torture, and the lack of a house. This study was supported by a study conducted in refugee camps in Europe [ 55 ]. Individuals who watched or personally participated in the murder of a family member or friend were more likely to experience anxiety than those who had not. This study was consistent with one carried out at the Ethiopian refugee camp of Maiayni [ 24 ]. Experiencing the murder of family or friends is an intensely traumatic event for anyone, and for refugees, it can be especially devastating, leading to heightened levels of anxiety among them [ 56 , 57 ]. This research also revealed that PTSD was positively associated with factors such as sex, age, employment status, length of stay at a refugee camp, experiences of abduction, rape, or sexual abuse, and the absence of food or water. This study was consistent with a study conducted in Darfur, Sudan [ 58 ], and Nepal [ 59 ].

Females were more likely to develop the odds of PTSD compared to males. This study is supported by a study done in Germany [ 60 ]. This could be due to the fact that women in refugee camps may face a higher risk of gender-based violence, including sexual assault and domestic violence. Such traumatic experiences can contribute significantly to the development of PTSD [ 60 ]. This could be due to the fact that women in refugee camps may face a higher risk of gender-based violence, including sexual assault and domestic violence. Such traumatic experiences can contribute significantly to the development of PTSD [ 61 , 62 ]. The odds of having PTSD among older Eritrean refugees were higher than among younger refugees. This study was comparable to a study done by J. M. Hegeman et al. [ 63 ]. As older individuals often experience a higher prevalence of chronic health conditions, pain, and physical limitations, these persistent illnesses can ultimately contribute to the development of PTSD [ 42 , 64 ]. Participants with a history of abduction, rape, or sexual abuse were more likely to have PTSD compared to those who did not have such a history. This study was in line with a study done in Mexico [ 44 ] and Sudan [ 22 ]. This could be attributed to the fact that refugees who have been the victims of sexual assault or kidnapping may suffer from miserable feelings of guilt and shame. These emotions have the potential to become internalized, which can result in a negative self-image and the emergence of variables that can exacerbate PTSD [ 22 , 65 ]. Lastly, compared to refugees who stayed in the camp for less than a year, those who spent a year or more there had a higher chance of developing PTSD. This study was similar to one done with North Korean refugees [ 66 ], Iraq refugees [ 67 ], and Eritrean refugees in Ethiopia [ 24 ]. Living for an extended period of time in refugee environments can subject individuals to persistent stressors, including challenging living conditions, struggles to meet essential survival needs such as obtaining water, food, shelter, and healthcare, an inability to generate income, and isolation from family and traditional social support systems. The cumulative impact of these challenges increases the susceptibility of individuals to mental health issues, including PTSD [ 42 , 68 , 69 ].

Limitation of the study

Due to the cross-sectional nature of the design, this study did not establish cause-and-effect relationships between our variables. Moreover, the study did not include data on the HIV status of participants because the study participants had not reported their HIV status during the data collection period.

Conclusion and recommendation

The results of this study revealed that more than one-third of Eritreans living in the refugee camp in Dabat town had symptoms of PTSD, anxiety, and depression. This prevalence is higher than the previously reported studies. Several factors have been identified as contributing to the development of depression, anxiety, and post-traumatic stress disorder (PTSD). These factors included older age, gender (specifically being female), monthly income levels, unemployment status, experiences of rape or sexual abuse, witnessing the murder of family or friends, enduring physical harm such as torture or beatings, incarceration, and deprivation of fundamental needs such as food, shelter, and water. As a result, it is critical to prioritize early screening and intervention for post-migration mental health, particularly for women who have experienced traumatic events.

This research emphasizes the need for both governmental and non-governmental organizations to secure the provision of essential necessities such as food, clean water, shelter, clothing, and education. Additionally, this study calls for the legal protection of Eritrean refugees against arson, sexual abuse, rape, imprisonment without due process, and abduction. This research also highlights the need for healthcare service providers to implement a psychosocial intervention within the refugee community to enhance living conditions and address the effects of traumatic stressors. Furthermore, the concerned organization may then put these approaches into action to help minimize the occurrence of PTSD, anxiety, and depression symptoms among Eritrean refugees via early detection, prevention, and intervention. This study recommends using ordinal logistic regression to delve into a more comprehensive understanding of the severity levels associated with depression, anxiety, and PTSD in future research.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Adjusted Odds Ratio

Confidence Interval

Crude Odds Ratio

Post-Traumatic Stress Disorder

Standard Deviation

Morris J. The value of refugees: UNHCR and the growth of the global refugee industry. J Refugee Stud. 2021;34(3):2676–98.

Article   Google Scholar  

Organization WH. World report on the health of refugees and migrants. 2022.

Konstantinov V, Reznik A, Isralowitz R. Update: civilian refugees of the Russian–Ukrainian war. J Loss Trauma. 2023;28(6):568–70.

Hall J, Werner K. Trauma and trust: how war exposure shapes social and institutional trust among refugees. Front Psychol. 2022;13:786838.

Article   PubMed   PubMed Central   Google Scholar  

Richards A, Ospina-Duque J, Barrera-Valencia M, Escobar-Rincón J, Ardila-Gutiérrez M, Metzler T, et al. Posttraumatic stress disorder, anxiety and depression symptoms, and psychosocial treatment needs in Colombians internally displaced by armed conflict: a mixed-method evaluation. Psychol Trauma: Theory Res Pract Policy. 2011;3(4):384.

Schick M, Zumwald A, Knöpfli B, Nickerson A, Bryant RA, Schnyder U, et al. Challenging future, challenging past: the relationship of social integration and psychological impairment in traumatized refugees. Eur J Psychotraumatology. 2016;7(1):28057.

Hameed S, Sadiq A, Din AU. The increased vulnerability of refugee population to mental health disorders. Kans J Med. 2018;11(1):20.

Article   PubMed Central   Google Scholar  

Rosenthal T, Touyz RM, Oparil S. Migrating populations and health: risk factors for cardiovascular disease and metabolic syndrome. Curr Hypertens Rep. 2022;24(9):325–40.

Bogic M, Njoku A, Priebe S. Long-term mental health of war-refugees: a systematic literature review. BMC Int Health Hum Rights. 2015;15(1):1–41.

Charuvastra A, Cloitre M. Social bonds and posttraumatic stress disorder. Annu Rev Psychol. 2008;59:301–28.

Cloitre M. Social Bonds and posttraumatic stress disorder. Ann Rev Psychol. 2008;59.

Johnson H, Thompson A. The development and maintenance of post-traumatic stress disorder (PTSD) in civilian adult survivors of war trauma and torture: a review. Clin Psychol Rev. 2008;28(1):36–47.

Article   PubMed   Google Scholar  

Ibrahim H, Hassan CQ. Post-traumatic stress disorder symptoms resulting from torture and other traumatic events among Syrian kurdish refugees in Kurdistan Region. Iraq Front Psychol. 2017;8:241.

PubMed   Google Scholar  

Konstantinov V, Reznik A, Isralowitz R. Depression and Quality of Life among Ukrainian adults relocated to Russia. J Loss Trauma. 2023:1–11.

Garnier A. 4. UNHCR and the transformation of global refugee governance: the case of refugee resettlement. Research Handbook on the Institutions of Global Migration Governance. 2023:50.

Gilbert G. The International Organization for Migration in Humanitarian Scenarios. 2023.

Morina N, Akhtar A, Barth J, Schnyder U. Psychiatric disorders in refugees and internally displaced persons after forced displacement: a systematic review. Front Psychiatry. 2018;9:433.

Organization WH. Monitoring mental health systems and services in the WHO European Region: Mental Health Atlas, 2017. World Health Organization. Regional Office for Europe; 2019.

Steel Z, Chey T, Silove D, Marnane C, Bryant RA, Van Ommeren M. Association of torture and other potentially traumatic events with mental health outcomes among populations exposed to mass conflict and displacement: a systematic review and meta-analysis. JAMA. 2009;302(5):537–49.

Article   CAS   PubMed   Google Scholar  

Organization WH. Promoting the health of refugees and migrants. Draft global action plan 2019–2023. 72nd World health assembly. 2019.

Thabet AAM, Abed Y, Vostanis P. Comorbidity of PTSD and depression among refugee children during war conflict. J Child Psychol Psychiatry. 2004;45(3):533–42.

Roberts B, Damundu EY, Lomoro O, Sondorp E. Post-conflict mental health needs: a cross-sectional survey of trauma, depression and associated factors in Juba, Southern Sudan. BMC Psychiatry. 2009;9(1):1–10.

Onyut LP, Neuner F, Ertl V, Schauer E, Odenwald M, Elbert T. Trauma, poverty and mental health among Somali and Rwandese refugees living in an African refugee settlement–an epidemiological study. Confl Health. 2009;3(1):1–16.

Berhe SM, Azale T, Fanta T, Demeke W, Minyihun A. Prevalence and predictors of depression among Eritrean refugees in Ethiopia: a cross-sectional survey. Psychol Res Behav Manage. 2021:1971–80.

Gleeson C, Frost R, Sherwood L, Shevlin M, Hyland P, Halpin R, et al. Post-migration factors and mental health outcomes in asylum-seeking and refugee populations: a systematic review. Eur J Psychotraumatology. 2020;11(1):1793567.

Miller KE, Weine SM, Ramic A, Brkic N, Bjedic ZD, Smajkic A, et al. The relative contribution of war experiences and exile-related stressors to levels of psychological distress among Bosnian refugees. J Trauma Stress: Official Publication Int Soc Trauma Stress Stud. 2002;15(5):377–87.

Teodorescu DS, Heir T, Hauff E, Wentzel-Larsen T, Lien L. Mental health problems and post‐migration stress among multi‐traumatized refugees attending outpatient clinics upon resettlement to Norway. Scand J Psychol. 2012;53(4):316–32.

Reedy J. The Mental Health conditions of Cambodian Refugee Children and adolescents. The Ohio State University; 2007.

Priebe S, Bogic M, Ajdukovic D, Franciskovic T, Galeazzi GM, Kucukalic A, et al. Mental disorders following war in the Balkans: a study in 5 countries. Arch Gen Psychiatry. 2010;67(5):518–28.

Ng LC, Stevenson A, Kalapurakkel SS, Hanlon C, Seedat S, Harerimana B, et al. National and regional prevalence of posttraumatic stress disorder in sub-saharan Africa: a systematic review and meta-analysis. PLoS Med. 2020;17(5):e1003090.

Lovibond SH. Manual for the depression anxiety stress scales. Sydney psychology foundation. 1995.

Oei TP, Sawang S, Goh YW, Mukhtar F. Using the depression anxiety stress scale 21 (DASS-21) across cultures. Int J Psychol. 2013;48(6):1018–29.

Severino GA, Haynes WDG. Development of an Italian version of the depression anxiety stress scales. Psychol Health Med. 2010;15(5):607–21.

Brumby S, Chandrasekara A, McCoombe S, Torres S, Kremer P, Lewandowski P. Reducing psychological distress and obesity in Australian farmers by promoting physical activity. BMC Public Health. 2011;11(1):1–7.

Simegn W, Dagnew B, Yeshaw Y, Yitayih S, Woldegerima B, Dagne H. Depression, anxiety, stress and their associated factors among Ethiopian University students during an early stage of COVID-19 pandemic: an online-based cross-sectional survey. PLoS ONE. 2021;16(5):e0251670.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Knappe F, Filippou K, Hatzigeorgiadis A, Morres ID, Tzormpatzakis E, Havas E, et al. Psychological well-being, mental distress, metabolic syndrome, and associated factors among people living in a refugee camp in Greece: a cross-sectional study. Front Public Health. 2023;11:1179756.

Georgiadou E, Zbidat A, Schmitt GM, Erim Y. Prevalence of mental distress among Syrian refugees with residence permission in Germany: a registry-based study. Front Psychiatry. 2018;9:393.

Steel Z, Silove D, Brooks R, Momartin S, Alzuhairi B, Susljik I. Impact of immigration detention and temporary protection on the mental health of refugees. Br J Psychiatry. 2006;188(1):58–64.

Ekblad S, Prochazka H, Roth G. Psychological impact of torture: a 3-month follow‐up of mass‐evacuated kosovan adults in Sweden. Lessons learnt for prevention. Acta Psychiatrica Scandinavica. 2002;106:30–6.

Mollica RF, Wyshak G, Lavelle J. The psychosocial impact of war trauma and torture on southeast Asian refugees. Am J Psychiatry. 1987;144(12):1567–72.

Albert PR. Why is depression more prevalent in women?: J Psychiatry Neurosci; 2015. p. 219–21.

Mahmood HN, Ibrahim H, Goessmann K, Ismail AA, Neuner F. Post-traumatic stress disorder and depression among Syrian refugees residing in the Kurdistan region of Iraq. Confl Health. 2019;13(1):1–11.

Hames JL, Hagan CR, Joiner TE. Interpersonal processes in depression. Ann Rev Clin Psychol. 2013;9:355–77.

Sabin M, Cardozo BL, Nackerud L, Kaiser R, Varese L. Factors associated with poor mental health among Guatemalan refugees living in Mexico 20 years after civil conflict. JAMA. 2003;290(5):635–42.

Cardozo BL, Bilukha OO, Crawford CAG, Shaikh I, Wolfe MI, Gerber ML, et al. Mental health, social functioning, and disability in postwar Afghanistan. JAMA. 2004;292(5):575–84.

Scholte WF, Olff M, Ventevogel P, de Vries G-J, Jansveld E, Cardozo BL, et al. Mental health symptoms following war and repression in eastern Afghanistan. JAMA. 2004;292(5):585–93.

Vinck P, Pham PN, Stover E, Weinstein HM. Exposure to war crimes and implications for peace building in northern Uganda. JAMA. 2007;298(5):543–54.

Logie CH, Okumu M, Loutet M, Berry I, Taing L, Lukone SO, et al. Associations between water insecurity and depression among refugee adolescents and youth in a humanitarian context in Uganda: cross-sectional survey findings. Int Health. 2023;15(4):474–6.

Rao TS, Asha M, Ramesh B, Rao KJ. Understanding nutrition, depression and mental illnesses. Indian J Psychiatry. 2008;50(2):77.

Logie CH, Okumu M, Latif M, Musoke DK, Odong Lukone S, Mwima S, et al. Exploring resource scarcity and contextual influences on wellbeing among young refugees in Bidi Bidi refugee settlement, Uganda: findings from a qualitative study. Confl Health. 2021;15:1–11.

Kim S, Thibodeau R, Jorgensen RS. Shame, guilt, and depressive symptoms: a meta-analytic review. Psychol Bull. 2011;137(1):68.

Fabbri C, Powell-Jackson T, Leurent B, Rodrigues K, Shayo E, Barongo V, et al. School violence, depression symptoms, and school climate: a cross-sectional study of Congolese and Burundian refugee children. Confl Health. 2022;16(1):1–11.

McFARLANE A, Clark CR, Bryant RA, Williams LM, Niaura R, Paul RH, et al. The impact of early life stress on psychophysiological, personality and behavioral measures in 740 non-clinical subjects. J Integr Neurosci. 2005;4(01):27–40.

Cabib S, Puglisi-Allegra S. The mesoaccumbens dopamine in coping with stress. Neurosci Biobehavioral Reviews. 2012;36(1):79–89.

Article   CAS   Google Scholar  

Nowak AC, Nutsch N, Brake T-M, Gehrlein L-M, Razum O. Associations between postmigration living situation and symptoms of common mental disorders in adult refugees in Europe: updating systematic review from 2015 onwards. BMC Public Health. 2023;23(1):1289.

Sheikh TL, Mohammed A, Agunbiade S, Ike J, Ebiti WN, Adekeye O. Psycho-trauma, psychosocial adjustment, and symptomatic post-traumatic stress disorder among internally displaced persons in Kaduna, Northwestern Nigeria. Front Psychiatry. 2014;5:127.

Elhabiby MM, Radwan DN, Okasha TA, El-Desouky ED. Psychiatric disorders among a sample of internally displaced persons in South Darfur. Int J Soc Psychiatry. 2015;61(4):358–62.

Hamid AA, Musa SA. Mental health problems among internally displaced persons in Darfur. Int J Psychol. 2010;45(4):278–85.

Thapa SB, Hauff E. Psychological distress among displaced persons during an armed conflict in Nepal. Soc Psychiatry Psychiatr Epidemiol. 2005;40:672–9.

Lukaschek K, Kruse J, Emeny RT, Lacruz ME, von Eisenhart Rothe A, Ladwig K-H. Lifetime traumatic experiences and their impact on PTSD: a general population study. Soc Psychiatry Psychiatr Epidemiol. 2013;48:525–32.

Ainamani HE, Elbert T, Olema DK, Hecker T. Gender differences in response to war-related trauma and posttraumatic stress disorder–A study among the Congolese refugees in Uganda. BMC Psychiatry. 2020;20:1–9.

Ssenyonga J, Owens V, Olema DK. Posttraumatic growth, resilience, and posttraumatic stress disorder (PTSD) among refugees. Procedia-Social and Behavioral Sciences. 2013;82:144–8.

Hegeman J, Kok R, Van der Mast R, Giltay E. Phenomenology of depression in older compared with younger adults: meta-analysis. Br J Psychiatry. 2012;200(4):275–81.

Byers AL, Covinsky KE, Neylan TC, Yaffe K. Chronicity of posttraumatic stress disorder and risk of disability in older persons. JAMA Psychiatry. 2014;71(5):540–6.

Taylor TF. The influence of shame on posttrauma disorders: have we failed to see the obvious? Eur J Psychotraumatology. 2015;6(1):28847.

Park K, Cho Y, Yoon I-J. Social inclusion and length of stay as determinants of health among North Korean refugees in South Korea. Int J Public Health. 2009;54:175–82.

Uribe Guajardo MG, Slewa-Younan S, Smith M, Eagar S, Stone G. Psychological distress is influenced by length of stay in resettled Iraqi refugees in Australia. Int J Mental Health Syst. 2016;10(1):1–7.

Miller KE, Rasmussen A. The mental health of civilians displaced by armed conflict: an ecological model of refugee distress. Epidemiol Psychiatric Sci. 2017;26(2):129–38.

Miller KE, Omidian P, Rasmussen A, Yaqubi A, Daudzai H. Daily stressors, war experiences, and mental health in Afghanistan. Transcult Psychiatry. 2008;45(4):611–38.

Goodyear MD, Krleza-Jeric K, Lemmens T. The declaration of Helsinki. British Medical Journal Publishing Group; 2007. pp. 624–5.

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Acknowledgements

The authors thank all study participants and data collectors for their contributions to the success of this study. The authors also thank the University of Gondar for providing ethical clearance.

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Conceptualization: Mihret Melese.Wudneh Simegn, Dereje Esubalehu, Wondim Ayenew Gashaw Sisay Chanie. Data curation: Abdulwase Mohammed Frmal analysis: Mihret Melese, Wudneh Simegn, Alemante Tafese Beyna. “Investigation: Mihret Melese, Wudneh Simegn, Yibeltal Yismaw Gela. Methodology: Yibeltal Yismaw Gela, Dereje Esubalehu, Melese Legesse Mitku. Project administration: Assefa kebad, Liknaw Workie Limenh. Resources: Wondim Ayenew, Alemante Tafese Beyna: Software: Yibeltal Yismaw Gela, Gashaw Sisay Chanie, Melese Legesse Mitku. Supervision: Yibeltal Yismaw Gela, Wudeneh Simegn, Wondim Ayenew: Validation: Abdulwase Mohammed, Alemante Tafese Beyna, Dereje Esubalew, Assefa Kebad. Visualization: Yibeltal Yismaw Gela. Writing– review & editing: Mihret Melese, Wudneh Simegn, Yibeltal Yismaw Gela, Dereje Esubalehu, Melese Legesse Mitku, Wondim Ayenew”.

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This research obtained ethical approval from the Institutional Review Board (IRB) at the University of Gondar, School of Medicine, College of Medicine, and Health Sciences ethical review committee (IRB/288/2023). Informed consent was obtained from all study participants, and for illiterate individuals, informed consent was obtained from the participant and/or their parent and/or legal guardian for their involvement in the study. All methods were performed in accordance with the relevant guidelines and regulations of the Helsinki Declaration [ 70 ].

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Melese, M., Simegn, W., Esubalew, D. et al. Symptoms of posttraumatic stress, anxiety, and depression, along with their associated factors, among Eritrean refugees in Dabat town, northwest Ethiopia, 2023. BMC Psychol 12 , 62 (2024). https://doi.org/10.1186/s40359-024-01554-7

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systematic review study characteristics table

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  1. Descriptive Table of Systematic Reviews

    systematic review study characteristics table

  2. -General Characteristics of Each Study Included in the Systematic

    systematic review study characteristics table

  3. Studies identified in systematic review and characteristics of each

    systematic review study characteristics table

  4. Characteristics of included studies in this systematic review

    systematic review study characteristics table

  5. Baseline characteristic of studies included in the systematic review

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  6. General Characteristics of the Studies Included in This Systematic

    systematic review study characteristics table

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  2. Systematic Review for Beginners

  3. SYSTEMATIC AND LITERATURE REVIEWS

  4. Methodology of the Study

  5. Systematic Study of Vedic Scriptures BG Chapter 15 verse 5 onwards

  6. Introduction Systematic Literature Review-Various frameworks Bibliometric Analysis

COMMENTS

  1. Chapter 9: Summarizing study characteristics and preparing for

    Stage 1. At protocol stage: Step 1.1. Set up the comparisons (Chapter 2 and Chapter 3).Stage 2. Summarizing the included studies and preparing for synthesis: Step 2.1. Summarize the characteristics of each study in a 'Characteristics of included studies' table (see Chapter 5), including examining the interventions to itemize their content and other characteristics (Section 9.3.1).

  2. Systematic Reviews: Data Extraction/Coding/Study characteristics/Results

    Here is an example of a table that summarizes the characteristics of studies in a review, note this table could be improved by adding a column for the quality score you assigned to each study, or you could add a column with a value representing the time period in which the study was carried out if this might be useful for the reader to know.

  3. Tables of included study characteristics for systematic reviews

    Appendix 2 Tables of included study characteristics for systematic reviews. TABLE 22. Characteristics of studies included in review 1. Study Participants Sample risk ... Tables of included study characteristics for systematic reviews - Routinely used interventions to improve attachment in infants and young children: a national survey and two ...

  4. 11.2.2 Characteristics of included studies tables

    Review authors should, as a minimum, include the following in the 'Characteristics of included studies' table: Methods: study design (stating whether or not the study was randomized), including, where relevant, a clear indication of how the study differs from a standard parallel group design (e.g. a cross-over or cluster-randomized design ...

  5. Systematic Reviews: Step 7: Extract Data from Included Studies

    These tables will help you determine which studies, if any, are eligible for quantitative synthesis. Data extraction requires a lot of planning. We will review some of the tools you can use for data extraction, the types of information you will want to extract, and the options available in the systematic review software used here at UNC, Covidence.

  6. Appendix 8: Included Study Characteristics Tables

    Study design: systematic reviews of qualitative studies, surveys or observational studies Included studies Number of included studies Total: 3 Systematic review: 1 Primary qualitative studies (not included in the systematic review): N = 2 Total number of participants Not reported Study design Qualitative primary studies and systematic reviews

  7. 4.6.1 Characteristics of included studies

    The 'Characteristics of included studies' table has five entries for each study: Methods, Participants, Interventions, Outcomes and Notes.

  8. Systematic Review

    Knowledge Base Methodology Systematic Review | Definition, Example, & Guide Systematic Review | Definition, Example & Guide Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023. A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence.

  9. Characteristics of included systematic reviews (review A)

    Aim of review: To conduct a systematic review of the best available evidence across all relevant disciplines to determine what characterises interventions effective in promoting walking; who walks more and by how much as a result of effective interventions; and the effects of such interventions on overall physical activity and health

  10. The PRISMA 2020 statement: an updated guideline ...

    The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement published in 2009 (hereafter referred to as PRISMA 2009) [4,5,6,7,8,9,10] is a reporting guideline designed to address poor reporting of systematic reviews [].The PRISMA 2009 statement comprised a checklist of 27 items recommended for reporting in systematic reviews and an "explanation and elaboration ...

  11. Introduction to systematic review and meta-analysis

    Introduction A systematic review collects all possible studies related to a given topic and design, and reviews and analyzes their results [ 1 ]. During the systematic review process, the quality of studies is evaluated, and a statistical meta-analysis of the study results is conducted on the basis of their quality.

  12. Guidance on Conducting a Systematic Literature Review

    Literature review is an essential feature of academic research. Fundamentally, knowledge advancement must be built on prior existing work. To push the knowledge frontier, we must know where the frontier is. By reviewing relevant literature, we understand the breadth and depth of the existing body of work and identify gaps to explore.

  13. Summary of Findings Table in a Systematic Review

    The Cochrane Review defines the "summary of findings table" as a structured tabular format in which the primary findings of a review, particularly information related to the quality of evidence, the magnitude of the effects of the studied interventions, and the aggregate of available data on the main outcomes, are presented.

  14. Data Extraction

    Completed data extraction forms can be used to produce a summary table of study characteristics that were considered important for inclusion in the Systematic Review. The completed summary table should be included in the Results section of the Report of the Systematic Review, either as an appendix or the in the body of the text.

  15. Systematic Reviews: Study selection and appraisal

    At the initial screening stage read just the title and abstract of the candidate studies and make a decision to include or exclude the study from your review. For small reviews of a few studies (e.g. <100) The research team should agree on the inclusion and exclusion criteria for studies you wish to review and put together a study screening form.

  16. Bleeding in patients on concurrent treatment with a selective serotonin

    In this study, we performed a systematic review and meta-analysis to elucidate whether concurrent treatment with an SSRI and ASA increases the risk of bleeding, compared with treatment with an SSRI or ASA alone. ... (Table S3). For instance, characteristics of the intervention and control groups were only provided in the study by Labos et al., ...

  17. Study characteristics in systematic review.

    Download Table | Study characteristics in systematic review. from publication: Brain structure in childhood maltreatment-related PTSD across the lifespan: A systematic review | Numerous ...

  18. Cardiac troponins and coronary artery calcium score: a systematic review

    The main characteristics of the studies included in the systematic review are summarized in Tables ... Table 1 Characteristics of the studies evaluating the association between hs-Tn T and CAC ... Study by Sandoval et al. has examined the relationship between hs-cTn T and CAC severity in 6,749 participants free of clinical cardiovascular ...

  19. Getting started with tables

    The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body...

  20. Maternal macronutrient and energy intake during pregnancy: a systematic

    A systematic review and meta-analysis was carried out based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. ... Study characteristics were extracted into a predetermined table in the Excel software that collected information including author, year of publication, participant number, study design ...

  21. Chapter 7: Considering bias and conflicts of interest among the

    Review authors should record these characteristics systematically for each study included in the systematic review (e.g. in the 'Characteristics of included studies' table) where appropriate. For example, trial registration status should be recorded for all randomized trials identified.

  22. Association between metabolic syndrome and ...

    The study was conducted according to the guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 . Study design and eligibility criteria. We included data from studies evaluated the association between metabolic syndrome and MI among participants with overweight or obesity, collectively mentioned as EBW.

  23. Cancer Screening Services: What Do Indigenous Communities Want? A

    The systematic review was undertaken using Preferred Reporting ... Each study was read and analyzed by two reviewers independently in detail to ascertain relevant characteristics such as study design, cancer type, country origin, sample size, and participant type. ... The details of the 18 studies included in the review are summarized in Table ...

  24. Procalcitonin for the diagnosis of postoperative bacterial infection

    Before commencing this work, the PROSPERO database [] was searched in March 2023, to identify any ongoing review with the same study question, but none was found.This review was designed and conducted following the Preferred Reporting for Systematic reviews and Meta-Analyses (PRISMA) [] and the Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy ...

  25. Efficacy and safety of the combination of camrelizumab and apatinib in

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  26. STUDY CHARACTERISTICS TABLE

    NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health. Walsh C, Lewinski AA, Rushton S, et al. Virtual Care for the Longitudinal Management of Chronic Conditions: A Systematic Review [Internet].

  27. Symptoms of posttraumatic stress, anxiety, and depression, along with

    In a similar systematic study carried out in 2019, ... Table 1 Background Characteristics of study participants in the Eritrea camp in Dabat town, northwest Ethiopia ... Harerimana B, et al. National and regional prevalence of posttraumatic stress disorder in sub-saharan Africa: a systematic review and meta-analysis. PLoS Med. 2020;17(5 ...

  28. Treatment for Childhood and Adolescent Dissociation: A Systematic Review

    Dissociation is thought to occur after a traumatic event(s) and can result in poor quality of life.This systematic review highlights the scant existing literature on treatment methods for children and adolescents with dissociative disorders.Seven articles described various treatments including psychotherapy, dialectical behavior therapy, eye movement desensitization and reprocessing, as well ...