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开发一种基于机器学习的预测模型,用于诱导后低血压

Maksim Katsin¹, Maxim Glebov², Haim Berkenstadt^2,3

¹Department of Anesthesiology, Sheba Medical Center, 21 Emek Dotan 11, Ramat Gan, Israel. katinml@gmail.com.

Journal of clinical monitoring and computing

|May 5, 2025

概括

机器学习有效地预测了诱导后低血压 (PIH),这是一个常见的手术并发症. 关键预测因素包括血压和普罗波福尔剂量,有助于风险分层.

科学领域:

麻醉学麻醉学
医疗信息学医疗信息学
心血管医学心血管医学

背景情况:

一般麻醉期间的动脉低血压会导致严重的术后并发症,如损伤,心肌损伤和中风.
诱导后低血压 (PIH) 是复杂的,受患者因素,药物和麻醉选择的影响,挑战了传统的预测模型.

研究的目的:

开发和验证用于预测PIH的机器学习 (ML) 模型.
使用ML识别PIH的关键临床预测因素.

主要方法:

对20309名成人患者进行了回顾性队列研究,这些患者接受了带有静脉诱导的非产科手术.
使用k倍交叉验证开发的ML模型;通过AUC评估性能;使用SHAP值评估特征重要性.
PIH定义为平均动脉压 (MAP) <55 mmHg在诱导后10分钟内.

主要成果:

在24.4%的患者中发生PIH (4,948/20,309).
确定了关键预测因子:诱导前的缩和平均动脉压,普罗波福尔剂量和β-阻断剂的使用.
在ML模型中,PIH预测的AUC为0.732.

结论:

基于ML的模型显示了PIH的显著预测能力.
这种模型可以增强术前规划和患者风险分层.
需要进行前性研究以进一步验证.

关键词:

机器学习是机器学习.诱导后的低血压是什么预测建模的预测建模.风险因素风险因素

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