An artificial intelligence and machine learning model for personalized prediction of long-term mitral valve repair durability.

Abstract

The study objective was to compare Random Survival Forest, a machine learning method, with Cox proportional hazards models in predicting long-term mitral valve repair durability, focusing on clinical utility and personalized decision-making. We analyzed 444 patients undergoing primary mitral valve repair for degenerative mitral regurgitation (2008-2024). The primary outcome was mitral repair failure, defined as recurrent regurgitation/stenosis or reintervention. Random Survival Forest and penalized Cox proportional hazards models were compared for predictive accuracy and interpretability. A web-based application was created to demonstrate the Random Survival Forest model. The failure end point, mitral repair failure, occurred in 13 individuals (3%) during the study period. Random Survival Forest showed superior discrimination (Concordance index: 0.874 vs 0.796) and identified both coaptation length and early mean mitral gradient as key predictors. Cox proportional hazards identified coaptation length alone, with each 1-mm increase reducing failure by approximately 40%. Random Survival Forest-predicted freedom from mitral repair failure at 5, 10, and 15 years was 94%, 74%, and 51% for coaptation length of 6 mm; 98%, 94%, and 91% for 9 mm; and 99%, 98%, and 96% for 12 mm, respectively. Mean gradients of 2 to 5 mm Hg were linked to 90% or greater durability at 5 to 10 years, whereas 8 mm Hg predicted worse outcomes (68% at 10 years, 64% at 15 years). Random Survival Forest further provided nuanced interpretation of temporal risk patterns and generated patient-specific survival estimates to improve repair durability forecasting. Machine learning outperforms traditional methods by modeling complex, nonlinear associations and identifying clinically actionable predictors. Integrating machine learning into surgical practice may support more personalized, data-driven mitral repair strategies and improve long-term outcomes.