Association of STS database variables with repair durability in ischemic mitral regurgitation using machine learning

Background: Machine learning (ML) can identify nonintuitive clinical variable combinations that predict clinical outcomes. To assess the potential predictive contribution of standardized Society of Thoracic Surgeons (STS) Database clinical variables, we used ML to detect their association with repair durability in ischemic mitral regurgitation (IMR) patients in a single institution study. Methods: STS Database variables (n = 53) served as predictors of repair durability in ML modeling of 224 patients who underwent surgical revascularization and mitral valve repair for IMR. Follow-up mortality and echocardiography data allowed 1-year outcome analysis in 173 patients. Supervised ML analyses were performed using recurrence (≥3+ IMR) or death versus nonrecurrence (<3+ IMR) as the binary outcome classification. Results: We tested standard ML and deep learning algorithms, including support vector machines, logistic regression, and deep neural networks. Following training, final models were utilized to predict class labels for the patients in the test set, producing receiver operating characteristic (ROC) curves. The three models produced similar area under the curve (AUC), and predicted class labels with promising accuracy (AUC = 0.72–0.75). Conclusions: Readily-available STS Database variables have potential to play a significant role in the development of ML models to direct durable surgical therapy in IMR patients.