Machine learning‑based prediction of new onset of atrial fibrillation after mitral valve surgery
10.1186/s42444-024-00127-4
- Publication Type:RESEARCH
- From:International Journal of Arrhythmia
2024;25(4):18-
- CountryRepublic of Korea
- Language:English
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Abstract:
Background:New-onset postoperative atrial fibrillation (nPOAF) is a common complication after cardiac surgery (30– 50%), being associated with unfavorable long-term outcomes. Using the Society of Thoracic Surgeons National Adult Cardiac Database, we used machine learning (ML) to predict nPOAF and related 30-day outcomes following mitral valve (MV) surgery. A total of 27,856 MV operations were performed at 910 centers between 7/1/2017 and 6/30/2020 on patients without AF or a prior permanent pacemaker. The primary endpoint was nPOAF postoperatively. ML tech‑ niques utilized included penalized logistic regression, gradient boosting, decision trees, and random forests.
Results:The overall incidence of nPOAF was 35.4% and that of new pacemaker insertion was 5.6%. Patients who developed nPOAF were older (67 ± 10 vs 60 ± 13 years), had more mitral valve stenosis (14.1% vs 11.7%), and hyperten‑ sion (72.1% vs 63.3%). They underwent more mitral valve replacement (39.1% vs 32.7%) and coronary artery bypass grafting (23.9% vs 16%). For predicting nPOAF, ML methods offer sensitivity, specificity and precision superior to logis‑ tic regression. The accuracy rate was identical with penalized and non-penalized logistic regression (0.672).
Conclusions:Predicting nPOAF and its short-term sequelae following MV surgery remains highly challenging.Machine learning methods offer a moderate degree of improvement in predicting nPOAF even in large national-level studies, in the absence of multi-modal data, such as real-time wearables data, electrocardiograms, heart rhythm moni‑ toring, or cardiac imaging.
