Evidence map›Paper›PMID 40705838›Full record

ArticleAnnals of medicine2025

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

Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

Abstract read
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Zheng ZhangDepartment of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Jian WuSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Yi DuanDepartment of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Linwei LiuInstitute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Yaru LiuSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Jinghan WangSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Li XiaoSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Zhifeng GaoDepartment of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.ORCID 0000-0001-6012-7466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh 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

methodsThis 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.

resultsAmong 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 ∼ 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.

conclusionThe 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.

Indexed as

Blood PressureHypertensionMachine LearningPostoperative ComplicationsSurgical Procedures, OperativeAdultAgedAlgorithmsFemaleHumansIntraoperative PeriodMaleMiddle AgedMonitoring, IntraoperativeRetrospective StudiesROC CurveAnesthesia managementblood pressure variabilityMachine learning

Identifiers

PMID40705838
PMCPMC12291218

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.