使用可解释的机器学习在非心脏外科手术中识别高手术内血压波动的模式:一个回顾性队列研究
Zheng Zhang1, Jian Wu2, Yi Duan1
1Department of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
Annals of medicine
|July 24, 2025
概括
高的手术内血压变化 (HIBPV) 会增加并发症的风险. 机器学习模型确定了心率和双谱指数等关键因素,有助于预测和管理非心脏手术期间的HIBPV.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗信息学 医疗信息学
- 心血管生理学心血管生理学
背景情况:
- 高的手术内血压变化 (HIBPV) 与不良的术后结果有关.
- 现有有限的实用工具来表征导致HIBPV的外科外科因素.
- 这项研究解决了对HIBPV的预测模型和模式识别的需求.
研究的目的:
- 开发可解释的监督机器学习 (ML) 模型来对HIBPV进行分类.
- 通过模型解释,识别与HIBPV相关的术后模式.
- 增强个性化的血液动力学管理策略.
主要方法:
- 对47520例非心脏手术病例进行了回顾性队列研究.
- 应用ML算法:XGBoost,RF,LightGBM,LR用于HIBPV的分类.
- 沙普利添加式解释 (SHAP) 用于模型解释和特征重要性评估.
主要成果:
- XGBoost 和 RF 显示出高性能 (AUROC 0.85 和 0.84).
- 关键预测变量包括手术内心率 (HR) 和双谱指数 (BIS).
- 血清剂量 (在50-70岁年龄组) 和手术内水平 (在高血压患者中) 与HIBPV风险有显著的关联.
结论:
- 开发的ML模型准确地对HIBPV进行了分类,并确定了关键的术后因素.
- 术内HR和BIS是HIBPV的重要贡献者.
- 涉及赛沃兰,年龄,高血压和水平的相互作用为个性化血液动力学管理提供了洞察力.
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