使用连续生命体征监测量量化大病房的生理稳定性:昼夜核心密度估计器
Søren S Rasmussen1, Katja K Grønbæk2, Jesper Mølgaard3
1Biomedical Signal Processing & AI Research Group, Digital Health Section, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345B, 2800 Kgs, Lyngby, Denmark. smora@dtu.dk.
Journal of clinical monitoring and computing
|June 2, 2023
概括
使用可穿戴生物传感器的持续监测需要新的分析方法. 一种新的昼夜核心密度估计 (KDE) 模型有效地预测了临床检测前几个小时的患者恶化,改善了医院患者监测.
科学领域:
- 生物医学工程 生物医学工程
- 生理监测 生理监测
- 数据分析 数据分析
背景情况:
- 住院患者的传统跟踪和触发系统正在被使用可穿戴生物传感器的持续监测所取代.
- 这种转变需要先进的实时数据流分析技术.
研究的目的:
- 使用核密度估计 (KDE) 设计一个生理稳定性指数.
- 将患者的昼夜节律纳入稳定性指数,以加强实时监测.
- 评估模型在预测不良事件方面的有效性.
主要方法:
- 利用来自491名术后患者和200名COPD患者的连续生命体征数据.
- 根据出院前的最后24小时而定义的生理稳定性.
- 对严重不良事件 (SAE) 和早期预警评分 (EWS) 事件 (≥6,≥8,≥10) 评估了模型.
主要成果:
- 昼夜KDE模型在稳定生理和EWS事件之间进行了良好的区分 (AUROC: 0.772-0.993).
- 对SAE的歧视程度较低 (AUROC:0.594-0.611).
- 预警时间为EWS事件的2.8-5.5小时,SAE事件的2.5小时.
结论:
- 昼夜KDE模型可以在临床工作人员通知前几个小时为严重的生命体标志偏差提供早期警告.
- 该模型显示了对不同患者队列的概括性.
- 这种方法提供了一种简单而有效的方法,用于在一般医院病房中对患者持续恶化的评估.
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