Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol
Bedford J., REDFERN O., PETRINIC T., CLIFTON D., WATKINSON P., COLLINS G.
Introduction Dozens of multivariable prediction models for atrial fibrillation after cardiac surgery (AFACS) have been published, but none have been incorporated into regular clinical practice. One of the reasons for this lack of adoption is poor model performance due to methodological weaknesses in model development. In addition, there has been little external validation of these existing models to evaluate their reproducibility and transportability. The aim of this systematic review is to critically appraise the methodology and risk of bias of papers presenting the development and/or validation of models for AFACS. Methods We will identify studies that present the development and/or validation of a multivariable prediction model for AFACS using a comprehensive bibliographic database search. Pairs of reviewers will independently extract model performance measures, assess methodological quality, and assess risk of bias of included studies using extraction forms adapted from a combination of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling studies (CHARMS) checklist and the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Extracted information will be reported by narrative synthesis and descriptive statistics. Ethics and dissemination This systemic review will only include published aggregate data, so no protected health information will be utilized. This review will identify weaknesses in past AFACS prediction model development and validation methodology so that subsequent AFACS prediction model development and validation can improve upon prior methodology and produce a clinically useful risk estimation tool. PROSPERO registration number: CRD42019127329.