Machine learning-based prediction of postoperative atrial fibrillation risk in coronary artery bypass grafting patients

Postoperative atrial fibrillation (POAF) occurs in 20–40% of patients undergoing coronary artery bypass grafting (CABG) and is associated with increased morbidity and mortality. Existing machine learning (ML) studies are limited by single‑center designs, small samples, and reliance on single algorithms. To develop and internally validate a stacking ensemble ML model for predicting POAF after CABG, identify key predictors, and evaluate its incremental clinical value against conventional models. A retrospective analysis of 563 CABG patients (29.5% POAF) from a single center was performed. After preprocessing, the dataset was split into training (n = 394) and validation (n = 169) sets. Features were selected using elastic net with stability selection (200 bootstrap resamples). Nine base ML algorithms and a stacking ensemble were built; hyperparameters were tuned via fivefold cross‑validation with overfitting controls. Model performance was assessed by discrimination, calibration, and decision curve analysis (DCA). Thirteen predictors were retained, with age, intraoperative phenylephrine use, and stroke history ranking highest. The stacking model achieved a validation AUC of 0.7425 and an F1 of 0.711. Logistic regression showed a comparable AUC (0.718, p = 0.09). The three clinical risk scores performed worse (AUCs 0.695, 0.662, 0.670; all p < 0.05). DCA revealed no net benefit below a threshold of 0.4, a marginal benefit (≈0.05) between 0.4 and 0.6, and no benefit above 0.6. The stacking model provides only moderate discrimination for POAF after CABG, with negligible net clinical benefit confined to a narrow threshold range. It is not ready for clinical use without external validation. The identified predictors may generate hypotheses for mechanistic studies, but their clinical utility remains unproven.