Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care

Rohan Joshi; Zheng Peng; Xi Long; Loe Feijs; Peter Andriessen; Carola Van Pul

doi:10.1109/JTEHM.2019.2953520

Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care

IEEE J Transl Eng Health Med. 2019 Nov 22:7:2700310. doi: 10.1109/JTEHM.2019.2953520. eCollection 2019.

Authors

Rohan Joshi^{1

2

3}, Zheng Peng⁴, Xi Long^{4

1}, Loe Feijs², Peter Andriessen⁵, Carola Van Pul^{3

6}

Affiliations

¹ 2Department of Family Care SolutionsPhilips Research5656AZEindhovenThe Netherlands.
² 3Department of Industrial DesignEindhoven University of Technology5612AZEindhovenThe Netherlands.
³ 5Department of Clinical PhysicsMáxima Medical Center5504DBVeldhovenThe Netherlands.
⁴ 1Department of Electrical EngineeringEindhoven University of Technology5612AZEindhovenThe Netherlands.
⁵ 4Department of NeonatologyMáxima Medical Center5504DBVeldhovenThe Netherlands.
⁶ 6Department of Applied PhysicsEindhoven University of Technology5612AZEindhovenThe Netherlands.

Abstract

We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 278,000 advisory (yellow) and 70,000 critical (red) alarms occurred. Vital signs including the heart rate, breathing rate, and oxygen saturation were obtained at a sampling frequency of 1 Hz while heart rate variability was calculated by processing the ECG - both were used for feature development and for predicting alarms. Yellow alarms that were followed by at least one red alarm within a short post-alarm window constituted the case-cohort while the remaining yellow alarms constituted the control cohort. For analysis, the case and control cohorts, stratified by proportion, were split into training (80%) and test sets (20%). Classifiers based on decision trees were used to predict, at the moment the yellow alarm occurred, whether a red alarm(s) would shortly follow. The best performing classifier used data from the 2-min window before the occurrence of the yellow alarm and could predict 26% of the red alarms in advance (18.4s, median), at the expense of 7% additional red alarms. These results indicate that based on predictive monitoring of critical alarms, nurses can be provided a longer window of opportunity for preemptive clinical action. Further, such as algorithm can be safely implemented as alarms that are not algorithmically predicted can still be generated upon the usual breach of the threshold, as in current clinical practice.

Keywords: Alarm fatigue; NICU; machine learning; medical devices; patient monitoring; predictive monitoring; real-time monitoring.

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