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

doi:10.1080/07853890.2025.2537920

	Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study
	Zhang, Zheng 1; Wu, Jian 2; Duan, Yi 1; Liu, Linwei 3; Liu, Yaru 2; Wang, Jinghan 2; Xiao, Li 2,3; Gao, Zhifeng 1
	2025-12-31
发表期刊	ANNALS OF MEDICINE
ISSN	0785-3890
卷号	57 期号:1 页码:12
摘要	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. Materials and Methods: This retrospective cohort study analyzed 47,520 noncardiac surgery cases from Beijing Tsinghua Changgung Hospital. We applied four ML algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Logistic Regression (LR)-to classify patients with or without HIBPV. The overall population and each age subgroup (pediatric, adult, elderly) underwent independent 70/30 train-test splits for model development. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC). SHapley Additive exPlanations (SHAP) values were used to interpret model outputs and assess feature importance. Results: Among 47,520 noncardiac surgeries, 1,996 (4.2%) were classified as HIBPV. XGBoost and RF achieved the best performance, with AUROC values of 0.85 (95% confidence intervals (CI): 0.84-0.86) and 0.84 (95% CI: 0.82-0.85). Intraoperative average heart rate (HR) and bispectral index (BIS) were the most influential variables. In patients aged 50 similar to 70, higher sevoflurane dosage was associated with reduced HIBPV risk. Among hypertensive patients, elevated intraoperative blood calcium (>1.10 mmol/L) was associated with increased HIBPV risk. Conclusion: The models enabled accurate classification of HIBPV cases and highlighted key discriminative perioperative variables through SHAP-based interpretation. Intraoperative HR and BIS were significant contributing factors. Moreover, interactions between sevoflurane and age and between hypertension and calcium levels may inform individualized hemodynamic management strategies.
关键词	Machine learning blood pressure variability Anesthesia management
DOI	10.1080/07853890.2025.2537920
收录类别	SCI
语种	英语
资助项目	Beijing Research Ward Excellence Program
WOS研究方向	General & Internal Medicine
WOS类目	Medicine, General & Internal
WOS记录号	WOS:001536415700001
出版者	TAYLOR & FRANCIS LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42049
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xiao, Li; Gao, Zhifeng
作者单位	1.Tsinghua Univ, Beijing Tsinghua Changgung Hosp, Sch Clin Med, Dept Anesthesiol,Tsinghua Med, Beijing 102218, Peoples R China 2.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Zheng,Wu, Jian,Duan, Yi,et al. Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study[J]. ANNALS OF MEDICINE,2025,57(1):12.
APA	Zhang, Zheng.,Wu, Jian.,Duan, Yi.,Liu, Linwei.,Liu, Yaru.,...&Gao, Zhifeng.(2025).Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.ANNALS OF MEDICINE,57(1),12.
MLA	Zhang, Zheng,et al."Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study".ANNALS OF MEDICINE 57.1(2025):12.