Machine learning models of postoperative atrial fibrillation prediction after cardiac surgery

Published:November 24, 2022DOI:



      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.


      Retrospective study.


      Anonymous hospital.


      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.



      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.


      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


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Journal of Cardiothoracic and Vascular Anesthesia
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Yin L
        • Ling X
        • Zhang Y
        • et al.
        CHADS2 and CHA2DS2-VASc scoring systems for predicting atrial fibrillation following cardiac valve surgery.
        PLoS One. 2015; 10e123858
        • Mathew JP
        • Fontes ML
        • Tudor IC
        • et al.
        A multicenter risk index for atrial fibrillation after cardiac surgery.
        JAMA. 2004; 291: 1720-1729
        • Kosmidou I
        • Chen S
        • Kappetein AP
        • et al.
        New-onset atrial fibrillation after PCI or CABG for left main disease: the EXCEL trial.
        J Am Coll Cardiol. 2018; 71: 739-748
        • Todorov H
        • Janssen I
        • Honndorf S
        • et al.
        Clinical significance and risk factors for new onset and recurring atrial fibrillation following cardiac surgery-a retrospective data analysis.
        BMC Anesthesiol. 2017; 17: 163
        • de Vos CB
        • Pisters R
        • Nieuwlaat R
        • et al.
        Progression from paroxysmal to persistent atrial fibrillation clinical correlates and prognosis.
        J Am Coll Cardiol. 2010; 55: 725-731
        • Mariscalco G
        • Biancari F
        • Zanobini M
        • et al.
        Bedside tool for predicting the risk of postoperative atrial fibrillation after cardiac surgery: the POAF score.
        J Am Heart Assoc. 2014; 3: e752
      1. Zacharias A, Schwann TA, Riordan CJ, et al. Obesity and risk of new-onset atrial fibrillation after cardiac surgery. Circulation 2005;112:3247-55.

      2. Bunn C, Kulshrestha S, Boyda J, et al. Application of machine learning to the prediction of postoperative sepsis after appendectomy. Surgery 2021;169:671-77.

        • Lee H
        • Yoon S
        • Yang S
        • et al.
        Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model.
        J Clin Med. 2018; 7: 428
        • Betts KS
        • Kisely S
        • Alati R.
        Predicting common maternal postpartum complications: leveraging health administrative data and machine learning.
        BJOG. 2019; 126: 702-709
        • Hindricks G
        • Potpara T
        • Dagres N
        • et al.
        2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS).
        Eur Heart J. 2021; 42: 373-498
        • Zou KH
        • O'Malley AJ
        • Mauri L.
        Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models.
        Circulation. 2007; 115: 654-657
        • Hanley JA
        • Mcneil BJ.
        The meaning and use of the area under a receiver operating characteristic (ROC) curve.
        Radiology. 1982; 143: 29-36
        • Delong ER
        • Delong DM
        • Clarke-Pearson DL.
        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
        Biometrics. 1988; 44: 837-845
        • Vickers AJ
        • Elkin EB.
        Decision curve analysis: A novel method for evaluating prediction models.
        Med Decis Making. 2006; 26: 565-574
        • Jerome HF.
        Greedy function approximation: a gradient boosting machine.
        Ann Stat. 2001; 29: 1189-1232
        • Cortes C
        • Vapnik V.
        Support-vector networks.
        Machine learning. 1995; 20: 273-297
        • Bewick V
        • Cheek L
        • Ball J.
        Statistics review 14: Logistic regression.
        Crit Care. 2005; 9: 112-118
        • van der Ploeg T
        • Austin PC
        • Steyerberg EW.
        Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints.
        BMC Med Res Methodol. 2014; 14: 137
        • Steyerberg EW
        • van der Ploeg T
        • Van Calster B.
        Risk prediction with machine learning and regression methods.
        Biom J. 2014; 56: 601-606
        • Christodoulou E
        • Ma J
        • Collins GS
        • et al.
        A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.
        J Clin Epidemiol. 2019; 110: 12-22
        • Austin PC
        • Tu JV
        • Ho JE
        • et al.
        Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes.
        J Clin Epidemiol. 2013; 66: 398-407
        • Axtell AL
        • Moonsamy P
        • Melnitchouk S
        • et al.
        Preoperative predictors of new-onset prolonged atrial fibrillation after surgical aortic valve replacement.
        J Thorac Cardiovasc Surg. 2020; 159: 1407-1414
        • Iliescu AC
        • Salaru DL
        • Achitei I
        • et al.
        Postoperative atrial fibrillation prediction following isolated surgical aortic valve replacement.
        Anatol J Cardiol. 2018; 19: 394-400
        • Kievišas M
        • Keturakis V
        • Vaitiekūnas E
        • et al.
        Prognostic factors of atrial fibrillation following coronary artery bypass graft surgery.
        Gen Thorac Cardiovasc Surg. 2017; 65: 566-574
        • Spach MS
        • Dolber PC.
        Relating extracellular potentials and their derivatives to anisotropic propagation at a microscopic level in human cardiac muscle. Evidence for electrical uncoupling of side-to-side fiber connections with increasing age.
        Circ Res. 1986; 58: 356-371
        • Hu Y
        • Chen Y
        • Lin Y
        • et al.
        Inflammation and the pathogenesis of atrial fibrillation.
        Nat Rev Cardiol. 2015; 12: 230-243
        • Lamm G
        • Auer J
        • Weber T
        • et al.
        Postoperative white blood cell count predicts atrial fibrillation after cardiac surgery.
        J Cardiothorac Vasc Anesth. 2006; 20: 51-56