Multivariable prediction models for atrial fibrillation after cardiac surgery: a systematic review protocol. Issue 3 (13th March 2023) - Fields, Kara G; Ma, Jie; Petrinic, Tatjana; Alhassan, Hassan; Eze, Anthony; Reddy, Ankith; Hedayat, Mona; Providencia, Rui; Lip, Gregory Y H; Bedford, Jonathan P; Clifton, David A; Redfern, Oliver C; et al.
Abstract : 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 through searches of PubMed, Embase and Web of Science from inception to 31 December 2021. 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 checklist and the Prediction Model Risk of Bias Assessment Tool. 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 used. Study findings will be disseminated through peer-reviewed publications and scientific conference presentations. Further, this review will identify weaknesses in past AFACS prediction model development and validation methodology so that subsequent studies can improve upon prior practices and produce a clinically useful risk estimation tool. PROSPERO registration number: CRD42019127329.