Machine Learning Models of Postoperative Atrial Fibrillation Prediction After Cardiac Surgery

Abstract

Objectives: This study aimed to use machine learning algorithms to build an efficient forecasting model of atrial fibrillation after cardiac surgery, and to compare the predictive performance of machine learning to traditional logistic regression.

Design: A retrospective study.

Setting: Second Affiliated Hospital of Zhejiang University School of Medicine.

Participants: The study comprised 1,400 patients who underwent valve and/or coronary artery bypass grafting surgery with cardiopulmonary bypass from September 1, 2013 to December 31, 2018.

Interventions: None.

Measurements and main results: Two machine learning approaches (gradient-boosting decision tree and support-vector machine) and logistic regression were used to build predictive models. The performance was compared by the area under the curve (AUC). The clinical practicability was assessed using decision curve analysis. Postoperative atrial fibrillation occurred in 519 patients (37.1%). The AUCs of the support-vector machine, logistic regression, and gradient boosting decision tree were 0.777 (95% CI: 0.772-0.781), 0.767 (95% CI: 0.762-0.772), and 0.765 (95% CI: 0.761-0.770), respectively. As decision curve analysis manifested, these models had achieved appropriate net benefit.

Conclusion: In the authors' study, the support-vector machine model was the best predictor; it may be an effective tool for predicting atrial fibrillation after cardiac surgery.

Keywords: cardiac surgery; machine learning; postoperative atrial fibrillation; predictive models.