A comprehensive ML-based Respiratory Monitoring System for Physiological Monitoring & Resource Planning in the ICU

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 SpO2/FiO2 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.