Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study
文献类型：期刊论文
| 作者 | Zhang, Zheng1; Wu, Jian2; Duan, Yi1; Liu, Linwei3; Liu, Yaru2; Wang, Jinghan2; Xiao, Li2,3; Gao, Zhifeng1 |
| 刊名 | ANNALS OF MEDICINE
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 57期号:1页码:12 |
| 关键词 | Machine learning blood pressure variability Anesthesia management |
| ISSN号 | 0785-3890 |
| DOI | 10.1080/07853890.2025.2537920 |
| 英文摘要 | 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. |
| 资助项目 | Beijing Research Ward Excellence Program |
| WOS研究方向 | General & Internal Medicine |
| 语种 | 英语 |
| WOS记录号 | WOS:001536415700001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42048]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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