Development and validation of an interpretable machine learning model to predict major adverse cardiovascular events after noncardiac surgery in geriatric patients: a prospective study.

Journal: International journal of surgery (London, England)
Published Date: Feb 1, 2025

Abstract

BACKGROUND: Major adverse cardiovascular events (MACEs) within 30 days following noncardiac surgery are prognostically relevant. Accurate prediction of risk and modifiable risk factors for postoperative MACEs is critical for surgical planning and patient outcomes. We aimed to develop and validate an accurate and easy-to-use machine learning model for predicting postoperative MACEs in geriatric patients undergoing noncardiac surgery.

Authors

  • Jiayu Yu
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Xiran Peng
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, PO Box 610041, Chengdu, China.
  • RuiHao Zhou
    Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Tao Zhu
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Xuechao Hao
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, PO Box 610041, Chengdu, China. aneshxc@163.com.

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