The Journal of thoracic and cardiovascular surgery · 2025
An artificial intelligence and machine learning model for personalized prediction of long-term mitral valve repair durability.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Malik Mohsyn Imran, Nedadur Rashmi, Chu Michael W A
Аннотация
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.