Development and Validation of a Machine Learning-based Explainable Predictive Model for Long-term net Adverse Clinical Events in Patients With High Bleeding Risk Undergoing Percutaneous Coronary Intervention: Results From a Prospective Cohort Study.

Journal: International journal of surgery (London, England)
Published Date: Jun 23, 2025

Abstract

BACKGROUND: Patients classified as having a high bleeding risk (HBR) and undergoing percutaneous coronary intervention (PCI) face a significantly greater incidence of net adverse clinical events (NACEs) than non-HBR patients do. Existing risk assessment models, such as the CRUSADE and TIMI scores, do not adequately address the unique risks faced by the HBR population. There is an urgent need for a precise and comprehensive predictive model tailored to PCI-HBR patients to guide clinical decision-making and improve patient outcomes.

Authors

  • Junyan Zhang
  • Yuting Lei
    Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, China.
  • Ran Liu
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Hongsen Zhao
    Information Center of West China Hospital, Sichuan University, Chengdu, China.
  • Yuxiao Li
  • Minggang Zhou
    Department of Cardiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Li Rao
    International Cooperation Base of Pesticide and Green Synthesis (Hubei), Key Laboratory of Pesticide & Chemical Biology (CCNU), Ministry of Education, Department of Chemistry, Central China Normal University, Wuhan 430079, China.
  • Dapeng Jiang
    DHC Mediway Technology Co Ltd, Beijing, China.
  • Zhongxiu Chen
    Department of Cardiology, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China.
  • Yong He
    College of Biosystems Engineering and Food Science, Zhejiang Univ., Hangzhou, 310058, China.

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