Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications.

Journal: British journal of anaesthesia
PMID: 38637267

Abstract

BACKGROUND: Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that use intraoperative respiratory features to predict PPCs.

Authors

  • Peiyi Li
    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.
  • Shuanliang Gao
    College of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China.
  • Yaqiang Wang
    College of Software Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China; Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service, Chengdu, Sichuan, 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.
  • Guo Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P.R.China.
  • Weimin Li
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, 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.
  • Tao Zhu
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.

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