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

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.

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

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12291218 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature