Risk Factor Analysis of Mitral Valve Repair Failure Based on Machine Learning
Objectives:To develop a novel prediction model for mitral valve repair failure based on machine learning algorithms.Methods:Clinical and echocardiographic data were analyzed on patients,who underwent mitral valve repair in Fuwai Hospital from 2009 January 1st to 2022 December 31st.End points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or severe mitral regurgitation before discharge).Risk factors of mitral valve repair failure were analyzed by XGBoost and shapley additive explanation (SHAP),and a machine learning model was established based on mixture of experts (MoE) as a risk prediction model and compared with conventional mitral valve repair complexity scores.Results:A total of 2314 patients were included in this study.Mitral repair was unsuccessful in 4.2％ (98 of 2314) of patients.Patient factors such as tricuspid regurgitation pressure gradient,A3 and A3P3 lesions,left ventricular end-systolic volume,and left atrium anterior and posterior diameter are associated with mitral valve repair failure;in addition,surgeon factors,such as cumulative repair failure rate,cumulative repair volume,and surgeon seniority,are also risk factors for mitral valve repair failure.The MoE model has an AUC value of 0.79,and the prediction performance is significantly better than traditional complexity scores.Conclusions:The MoE based machine learning model can predict the risk of mitral valve repair failure well.This evaluation system can effectively assist surgeons in assessing the risk of mitral valve repair failure and in selecting suitable treatment options for patients.
万方数据
维普
