Wireless monitoring and Real-time Adaptive Predictive Indicator of Deterioration

Duncan, Heather and Fule, Balazs and Rice, Iain and Stich, Alice and Lowe, David (2020) Wireless monitoring and Real-time Adaptive Predictive Indicator of Deterioration. Nature, 10 (11366). ISSN 1476-4687

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To assist in the early warning of deterioration in hospitalised children we studied the feasibility of collecting continuous wireless physiological data using Lifetouch (ECG-derived heart and respiratory rate) and WristOx2 (pulse-oximetry and derived pulse rate) sensors. We compared our bedside paediatric early warning (PEW) score and a machine learning automated approach: a Real-time Predictive Indicator of Deterioration (RAPID) to identify children experiencing significant clinical deterioration.
982 patients contributed 7,073,486 minutes during 1263 monitoring sessions. The proportion of intended monitoring time was 93 % for Lifetouch and 55% for WristOx2. Valid clinical data was 63 % of intended monitoring time for Lifetouch and 50% WristOx2. 29 patients experienced 36 clinically significant deteriorations. RAPID Index detected significant deterioration more frequently (77 to 97%) and earlier than the PEW score 9/26. High sensitivity and negative predictive value for RAPID Index was associated with low specificity and low positive predictive value.
We conclude that it is feasible to collect clinically valid physiological data wirelessly for 50% of intended monitoring time. The RAPID Index identified more deterioration, before the PEW score, but has a low specificity. By using the RAPID Index with a PEW system some life-threatening events may be averted.

Item Type: Article
Identification Number: https://doi.org/10.1038/s41598-020-67835-4
23 June 2020Accepted
9 July 2020Published Online
Uncontrolled Keywords: biomarkers, medical research, paediatric research, predicitive markers, prognostic markers
Subjects: CAH01 - medicine and dentistry > CAH01-01 - medicine and dentistry > CAH01-01-01 - medical sciences (non-specific)
CAH09 - mathematical sciences > CAH09-01 - mathematical sciences > CAH09-01-03 - statistics
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Iain Rice
Date Deposited: 26 Jun 2020 14:38
Last Modified: 12 Jan 2022 12:58
URI: https://www.open-access.bcu.ac.uk/id/eprint/9388

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