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

Research output: Contribution to journalArticlepeer-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 languageEnglish
Article number44
Number of pages8
JournalBMC Medical Research Methodology
Volume23
Issue number1
DOIs
Publication statusPublished - 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

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