CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models

Borja M. Fernandez-Felix; Jesus López-Alcalde; Marta Roqué; Alfonso Muriel; Javier Zamora

doi:10.1186/s12874-023-01849-0

CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models

Borja M. Fernandez-Felix^*, Jesus López-Alcalde, Marta Roqué, Alfonso Muriel, Javier Zamora

^*Corresponding author for this work

Metabolism and Systems Science

Research output: Contribution to journal › Article › peer-review

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Abstract

Background: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models.

Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication.

Conclusion: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.

Original language	English
Article number	44
Number of pages	8
Journal	BMC Medical Research Methodology
Volume	23
Issue number	1
DOIs	https://doi.org/10.1186/s12874-023-01849-0
Publication status	Published - 17 Feb 2023

Bibliographical note

Funding Information:
This study has been funded by Instituto de Salud Carlos III through the project PI19/00481, co-funded by European Regional Development Fund "A way to make Europe". BMFF works for and is remunerated by CIBER (Biomedical Research Network in Epidemiology and Public Health). These public funding bodies had no role in the study design, analysis and the interpretation of the results, and the writing of the report nor the decision to submit the paper for publication.

Publisher Copyright:
© 2023, The Author(s).

Keywords

CHARMS
PROBAST
Prognostic model
Systematic review
Template

ASJC Scopus subject areas

Epidemiology
Health Informatics

Access to Document

10.1186/s12874-023-01849-0Licence: Creative Commons: Attribution (CC BY)

FernandezFelixB2023CHARMSFinal published version, 5.99 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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abstract = "Background: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models.Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication.Conclusion: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.",

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note = "Funding Information: This study has been funded by Instituto de Salud Carlos III through the project PI19/00481, co-funded by European Regional Development Fund {"}A way to make Europe{"}. BMFF works for and is remunerated by CIBER (Biomedical Research Network in Epidemiology and Public Health). These public funding bodies had no role in the study design, analysis and the interpretation of the results, and the writing of the report nor the decision to submit the paper for publication. Publisher Copyright: {\textcopyright} 2023, The Author(s).",

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AU - Fernandez-Felix, Borja M.

AU - López-Alcalde, Jesus

AU - Roqué, Marta

AU - Muriel, Alfonso

AU - Zamora, Javier

N1 - Funding Information: This study has been funded by Instituto de Salud Carlos III through the project PI19/00481, co-funded by European Regional Development Fund "A way to make Europe". BMFF works for and is remunerated by CIBER (Biomedical Research Network in Epidemiology and Public Health). These public funding bodies had no role in the study design, analysis and the interpretation of the results, and the writing of the report nor the decision to submit the paper for publication. Publisher Copyright: © 2023, The Author(s).

PY - 2023/2/17

Y1 - 2023/2/17

N2 - Background: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models.Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication.Conclusion: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.

AB - Background: Systematic reviews of studies of clinical prediction models are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS and PROBAST are the standard tools used for these steps in these reviews of clinical prediction models.Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models including both recommended tools. The template makes it easier for reviewers to extract data, to assess the risk of bias and applicability, and to produce results tables and figures ready for publication.Conclusion: We hope this template will simplify and standardize the process of conducting a systematic review of prediction models, and promote a better and more comprehensive reporting of these systematic reviews.

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CHARMS and PROBAST at your fingertips: a template for data extraction and risk of bias assessment in systematic reviews of predictive models

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this