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Background <p>Post-operative moderate-to-severe mitral regurgitation (MR) following transcatheter aortic valve replacement (TAVR) is associated with poor outcomes, yet the factors contributing to this complication are not well understood. This study aimed to identify risk factors and develop predictive models for post-operative MR following TAVR using machine learning (ML) techniques to enhance early detection and intervention.</p> Methods <p>We retrospectively analyzed data from patients who underwent TAVR at our center between August 2014 and August 2023. Patients were classified into post-operative and nonpost-operative MR groups based on postprocedural MR severity. Various ML models were evaluated for predictive performance using metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanation (SHAP) values were used to interpret predictive patterns and develop a clinically relevant model.</p> Results <p>Among the evaluated models, the random forest model exhibited the highest predictive performance for post-operative moderate-to-severe MR after TAVR. Key predictors, which were confirmed by the SHAP analysis as important in the predictive framework, included echocardiographic parameters, blood test results, patient age, and body mass index.</p> Conclusions <p>ML models show promise in predicting post-operative moderate-to-severe MR after TAVR by integrating clinical indicators to enhance predictive accuracy.</p> Clinical trial number <p>Not applicable.</p>

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Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月10日

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  • Zhenzhen Li,
  • Jianing Fan,
  • Jiajun Fan,
  • Jiaxin Miao,
  • Dawei Lin,
  • Jingyan Zhao,
  • Xiaochun Zhang,
  • Wenzhi Pan,
  • Daxin Zhou,
  • Junbo Ge

摘要

Background

Post-operative moderate-to-severe mitral regurgitation (MR) following transcatheter aortic valve replacement (TAVR) is associated with poor outcomes, yet the factors contributing to this complication are not well understood. This study aimed to identify risk factors and develop predictive models for post-operative MR following TAVR using machine learning (ML) techniques to enhance early detection and intervention.

Methods

We retrospectively analyzed data from patients who underwent TAVR at our center between August 2014 and August 2023. Patients were classified into post-operative and nonpost-operative MR groups based on postprocedural MR severity. Various ML models were evaluated for predictive performance using metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Shapley Additive Explanation (SHAP) values were used to interpret predictive patterns and develop a clinically relevant model.

Results

Among the evaluated models, the random forest model exhibited the highest predictive performance for post-operative moderate-to-severe MR after TAVR. Key predictors, which were confirmed by the SHAP analysis as important in the predictive framework, included echocardiographic parameters, blood test results, patient age, and body mass index.

Conclusions

ML models show promise in predicting post-operative moderate-to-severe MR after TAVR by integrating clinical indicators to enhance predictive accuracy.

Clinical trial number

Not applicable.