Interpretable machine learning prediction model for major adverse cardiovascular events in patients with peripheral artery disease.

Journal: Journal of vascular surgery
Published Date: May 21, 2025

Abstract

BACKGROUND: Major adverse cardiovascular events (MACEs) are severe complications of peripheral arterial disease (PAD), associated with a poor prognosis and disease burden. Therefore, the early identification of high-risk individuals is of paramount importance. This study aimed to develop and validate an interpretable machine learning (ML)-based prediction model for MACE risk in patients with PAD.

Authors

  • Pan Song
    Southwest Medical University, Luzhou, Sichuan Province, China.
  • Xinjun Liu
    Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Lu Tang
    Department of Communication and Journalism, Texas A&M University.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Qin Chen
    School of Life Sciences, Shanghai University, Shanghai 200444, China. Electronic address: chenqincc@edu.cn.
  • Xiaoyu Liu
    State Grid Hebei Electric Power Co., Ltd., Marketing Service Center, Shijiazhuang 050035, China.
  • Xiaoyan Quan
    Southwest Medical University, Luzhou, Sichuan Province, China.
  • Yuxin Niu
    State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Department and Institute of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Chi Cui
    The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan Province, China.
  • Meihong Shi
    Southwest Medical University, Luzhou, Sichuan Province, China. Electronic address: shimeihong@swmu.edu.cn.

Keywords

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