Key result
Consumer-grade wearables predict COVID-19 inpatient discharge or ICU admission with ~0.90 AUROC.
Why the study?
Existing risk-scoring approaches for inpatients with COVID-19 focus primarily on ICUs with specialized devices rather than hospital general wards.
Does a risk score based on consumer-grade wearables accurately predict hospital discharge or ICU admission in inpatients with COVID-19?
Observational (n=40)
No
Does a risk score based on consumer-grade wearables accurately predict hospital discharge or ICU admission in inpatients with COVID-19?
A risk score derived from consumer-grade smartwatch physiological data (HR, HRV, RF) showed good predictive performance for ICU admission or discharge in COVID-19 inpatients.
May aid wearable-based risk stratification in COVID-19 inpatients; hypothesis-generating and requires prospective validation before adoption.
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. CLINICALTRIAL Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834
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Föll et al. (2021) conducted an observational in COVID-19 (n=40). Consumer-grade wearables (smartwatches) risk score was evaluated on Probability of hospital discharge or ICU admission (AUROC 0.73-0.90). A risk-scoring system based on consumer-grade wearables achieved a time-dependent AUROC of 0.73-0.90 for predicting hospital discharge or ICU admission in inpatients with COVID-19.
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