Machine Learning‐Based Prediction of Mortality in Patients With Mitral Regurgitation: Results From Two Nationwide, Prospective Studies

ABSTRACT Mitral regurgitation (MR) is among the most prevalent valvular heart diseases. We developed and externally validated a machine learning (ML)‐based mortality prediction model for patients with significant MR. From the ChinaValvular Heart Disease (China‐VHD) registry, 6323 patients with moderate or greater MR were randomly split 7:3 into training and internal validation sets. Variables selected concordantly by Cox stepwise regression, Least Absolute Shrinkage and Selection Operator (LASSO)‐penalized Cox regression, and Boruta were retained. Seventeen ML and traditional models were compared. The final model was tested in the internal set and an external cohort of 4658 patients from the China Elderly Valve Disease (China‐DVD) study. SHapley Additive exPlanations (SHAP) analysis assessed interpretability. Fifteen predictors were identified. CatBoost performed best, with AUCs of 0.783 (95% confidence interval [CI] 0.751–0.815) internally and 0.703 (0.678–0.728) externally, outperforming Cox (0.699), MitraScore (0.620), and EuroSCORE II (0.679). Age, NYHA class, and left ventricular ejection fraction were the top predictors. The model effectively distinguished high‐risk from low‐risk patients internally (adjusted hazard ratio [HR] 5.49, p < 0.001) and externally (adjusted HR 3.05, p < 0.001). In conclusion, we developed and externally validated an ML‐based mortality prediction model for MR irrespective of etiology or treatment strategy, with potential to improve risk stratification and clinical decision‐making.