Background: Clinical prediction model systematic reviews are becoming increasingly abundant in the literature. Data extraction and risk of bias assessment are critical steps in any systematic review. CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) and PROBAST (Prediction model Risk Of Bias Assessment Tool) are the standard tools used for these steps in reviews of clinical prediction models. Objective: The aim of our study was to create an Excel template for extracting data and assessing the risk of bias and the applicability of predictive models using these two tools (i.e., CHARMS and PROBAST).
Methods: The Excel file (named CHARMS and PROBAST template.xls) consists of eight sheets. The first sheet, “Home”, provides a description of the Excel file, instructions for its use, and links to relevant papers and forms. The following three sheets (“Summary”, “CHARMS”, and “PROBAST”) correspond to the collection of data from the studies included in the systematic review, and the following three sheets (“Study Characteristics”, “Model characteristics “, and “PROBAST summary”) contain the tables and figures generated automatically from the data collected. The final sheet (“CHARMS. Drop-down response lists”) allows tailoring of the template to the systematic review.
Results: We developed an Excel template for data extraction and risk of bias assessment of clinical prediction models. The template makes it easier for reviewers to extract data, assess the risk of bias and applicability, and automatically produce tables and figures of the results that show a summary of the characteristics of included studies, models, and their critical appraisal. These tables and figures are ready for publication. The template, as well as an example with a filled in file, can be downloaded from https://github.com/Fernandez-Felix/CHARMS-and-PROBAST-template.
Conclusions: 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. Patient, public, and/or healthcare consumer involvement: Not foreseen.