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Föll, Simon ORCID logoORCID: https://orcid.org/0000-0002-4364-4282; Lison, Adrian ORCID logoORCID: https://orcid.org/0000-0002-6822-8437; Maritsch, Martin ORCID logoORCID: https://orcid.org/0000-0001-9920-0587; Klingberg, Karsten ORCID logoORCID: https://orcid.org/0000-0002-2502-1428; Lehmann, Vera ORCID logoORCID: https://orcid.org/0000-0002-6038-809X; Züger, Thomas ORCID logoORCID: https://orcid.org/0000-0001-6190-7405; Srivastava, David ORCID logoORCID: https://orcid.org/0000-0002-4744-0463; Jegerlehner, Sabrina ORCID logoORCID: https://orcid.org/0000-0002-0868-0608; Feuerriegel, Stefan ORCID logoORCID: https://orcid.org/0000-0001-7856-8729; Fleisch, Elgar ORCID logoORCID: https://orcid.org/0000-0002-4842-1117; Exadaktylos, Aristomenis ORCID logoORCID: https://orcid.org/0000-0002-2705-5170 and Wortmann, Felix ORCID logoORCID: https://orcid.org/0000-0001-5034-2023 (2022): A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. In: JMIR Formative Research, Vol. 6, No. 6, e35717 [PDF, 875kB]


Background: To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. Objective: In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). Methods: Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. Results: First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. Conclusions: Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system.

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