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Machine learning models of postoperative atrial fibrillation prediction after cardiac surgery

Published:November 24, 2022DOI:https://doi.org/10.1053/j.jvca.2022.11.025

      Abstract

      Objectives

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

      Design

      Retrospective study.

      Setting

      Anonymous hospital.

      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). And the clinical practicability was assessed using decision curve analysis. Postoperative atrial fibrillation occurred in 519 patients (37.1%). The AUCs of support vector machine, logistic regression and gradient boosting decision tree were 0.777 [95% confidence interval (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 our study, support vector machine model was the best predictor, it may be an effective tool for predicting atrial fibrillation after cardiac surgery.

      Graphical abstract

      Keywords

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