Machine Learning-Based Prognostic Models for Mortality in Patients Receiving Implantable Cardioverter Defibrillators.

Journal: Pacing and clinical electrophysiology : PACE
Published Date: Jul 8, 2025

Abstract

BACKGROUND: Accurately predicting the clinical trajectory of patients with implantable cardioverter-defibrillators (ICDs) is critical for guiding their care and management. Machine learning (ML) methods surpass traditional statistical approaches by addressing complex data patterns and variability, providing more precise and personalized risk estimates.

Authors

  • Lei Pan
    Department of Chemical Engineering, Michigan Technological University, Houghton, MI 49931, USA.
  • Xi Liu
    Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Suzhou Medical College, Soochow University, Suzhou, Jiangsu 215123, China.
  • Li Zhu
    Medical College, Yangzhou University, Yangzhou 225001, China.
  • Ziqing Yu
    Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China.
  • Jingfeng Wang
    Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou 510120, China. Electronic address: dr_wjf@hotmail.com.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Weiwei Zhang
    Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
  • Ruogu Li
    Department of Cardiology, Shanghai Chest Hospital of Shanghai Jiao Tong University, Shanghai, China.
  • Zhongkai Wang
    Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore.
  • Hongyang Lu
    Medtronic Technology Center, Cardiac Rhythm Management, Medtronic (Shanghai) Ltd., Shanghai, China.
  • Shengwen Yang
    Laboratory of Xinjiang Endemic Phytomedicine Resources Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832003 Xinjiang, China. Electronic address: shengwenyang@shzu.edu.cn.
  • Peizhao Li
    Department of Information and Intelligence Development, Zhongshan Hospital of Fudan University, Shanghai, China.
  • Yangang Su
    Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China.
  • Wei Hua
    Food and Drug Administration, Silver Spring, Maryland, USA.
  • Yixiu Liang
    Department of Cardiology, Zhongshan Hospital of Fudan University, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai, China.

Keywords

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