• Yufan Lu, Qingjuan Chen, Hu Zhang, Meijiao Huang, Yu Yao, Yue Ming, Min Yan, Yunxian Yu, and Lina Yu.
• Department of Anesthesiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Zhejiang, China; Department of Anesthesiology, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
• J. Cardiothorac. Vasc. Anesth. 2023 Mar 1; 37 (3): 360366360-366.

ObjectivesThis 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.DesignA retrospective study.SettingSecond Affiliated Hospital of Zhejiang University School of Medicine.ParticipantsThe 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.InterventionsNone.Measurements And Main ResultsTwo 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.ConclusionIn 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.Copyright © 2022 Elsevier Inc. All rights reserved.

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