医学
重症监护室
队列
警报
呼吸衰竭
急诊医学
重症监护医学
机械通风
通风(建筑)
持续监测
内科学
运营管理
工程类
机械工程
航空航天工程
作者
Matthias Hüser,Xinrui Lyu,Martin Faltys,Alizée Pace,Marine Hoche,Stephanie Hyland,Hugo Yèche,M. Burger,Tobias M. Merz,Gunnar Rätsch
出处
期刊:Cold Spring Harbor Laboratory - medRxiv
日期:2024-01-23
被引量:2
标识
DOI:10.1101/2024.01.23.24301516
摘要
Abstract Respiratory failure (RF) is a frequent occurrence in critically ill patients and is associated with significant morbidity and mortality as well as resource use. To improve the monitoring and management of RF in intensive care unit (ICU) patients, we used machine learning to develop a monitoring system covering the entire management cycle of RF, from early detection and monitoring, to assessment of readiness for extubation and prediction of extubation failure risk. For patients in the ICU in the study cohort, the system predicts 80% of RF events at a precision of 45% with 65% identified 10h before the onset of an RF event. This significantly improves upon a standard clinical baseline based on the SpO 2 /FiO 2 ratio. After a careful analysis of ICU differences, the RF alarm system was externally validated showing similar performance for patients in the external validation cohort. Our system also provides a risk score for extubation failure for patients who are clinically ready to extubate, and we illustrate how such a risk score could be used to extubate patients earlier in certain scenarios. Moreover, we demonstrate that our system, which closely monitors respiratory failure, ventilation need, and extubation readiness for individual patients can also be used for ICU-level ventilator resource planning. In particular, we predict ventilator use 8-16h into the future, corresponding to the next ICU shift, with a mean absolute error of 0.4 ventilators per 10 patients effective ICU capacity.
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