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

Journal: BMC cardiovascular disorders
PMID: 40348949

Abstract

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.

Authors

  • Zhenzhen Li
    School of Materials and Chemical Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou, 450001, China.
  • Jianing Fan
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Jiajun Fan
    Chongqing University, Chongqing, 400030, China.
  • Jiaxin Miao
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Dawei Lin
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Jingyan Zhao
    Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
  • Xiaochun Zhang
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Wenzhi Pan
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China. peden@sina.com.
  • Daxin Zhou
    School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. daxin_zhou@163.com.
  • Junbo Ge
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.

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