Machine learning and Regression–Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis

Abstract

Background: Postoperative atrial fibrillation (POAF) is a common complication following coronary artery bypass grafting (CABG) and is associated with adverse clinical outcomes. Traditional risk prediction models show limited accuracy, prompting increasing interest in machine learning based approaches. This systematic review and meta analysis aimed to evaluate the diagnostic performance, methodological quality, and clinical applicability of machine learning models for predicting POAF after CABG. Methods: A comprehensive literature search identified observational studies developing or validating machine learning or advanced statistical models for POAF prediction after CABG. Diagnostic performance measures were pooled using random effects bivariate models. Risk of bias and applicability were assessed using PROBAST and PROBAST AI. Between study heterogeneity, sensitivity analyses, meta regression, and publication bias were evaluated. Results: = 87.3%). Most studies were judged at high risk of bias, primarily due to limitations in analysis methods and validation strategies, resulting in overall certainty of evidence rated as moderate to low. Conclusions: Machine learning models demonstrate moderate accuracy for predicting POAF after CABG but are limited by heterogeneity, methodological shortcomings, and restricted external validation. Further rigorously designed and prospectively validated studies are needed to support clinical implementation.

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