A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study.

Journal: BMC geriatrics
PMID: 38918723

Abstract

BACKGROUND: Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients.

Authors

  • Xiran Peng
    Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, PO Box 610041, Chengdu, China.
  • Tao Zhu
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Qixu Chen
    Center of Statistical Research, School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
  • Yuewen Zhang
    Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK. tpjk2@cam.ac.uk.
  • 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.
  • Ke Li
    School of Ideological and Political Education, Shanghai Maritime University, Shanghai, 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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