Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.

Journal: Annals of medicine
Published Date: Jul 24, 2025

Abstract

BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study aimed to develop explainable supervised machine learning (ML) models to classify patients with HIBPV and to identify structural perioperative patterns associated with HIBPV through model interpretation.

Authors

  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Jian Wu
    Department of Medical Technology, Jiangxi Medical College, Shangrao, Jiangxi, China.
  • Yi Duan
    Department of Spinal Surgery, Affiliated Hospital of Southwest Medical University, Luzhou Sichuan, 646000, P.R.China.
  • Linwei Liu
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
  • Yaru Liu
    Department of Acupuncture, The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing 100029, China.
  • Jinghan Wang
    Department of Ophthalmology, Fudan Eye & ENT Hospital, Shanghai, China.
  • Li Xiao
    Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
  • Zhifeng Gao
    Microsoft Research, Beijing, China.

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