A Scalable Risk Scoring System for COVID-19 Inpatients Based on Consumer-grade Wearables: Statistical Analysis and Model Development.

Abstract

BACKGROUND

To provide effective care for COVID-19 inpatients, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in COVID-19 patients focus primarily on intensive care units with specialized medical measurement devices, but not on hospital general wards.

OBJECTIVE

In this paper, we aim to develop a risk score for COVID-19 inpatients in general wards based on consumer-grade wearables (smartwatches).

METHODS

Patients wore consumer-grade wearables to record physiological measurements such as heart rate, heart rate variability, and respiration frequency. Based on Bayesian survival analysis, we validate the association between these measurements and the patient outcomes (i.e., discharge or intensive care unit admission). To build our risk score, we generate a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates infers the probability of either hospital discharge or intensive care unit (ICU) admission.

RESULTS

We evaluate the predictive performance of our developed system for risk scoring in a single-center, prospective study based on N = 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. First, the Bayesian survival analysis shows that physiological measurements from consumer-grade wearables are significantly associated with the patient outcomes (i.e., discharge or intensive care unit admission). Second, our risk score achieves a time-dependent area under the receiver operating characteristic curve of 0.73 to 0.90 based on leave-one-subject-out cross-validation.

CONCLUSIONS

Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in COVID-19 inpatients. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system.

CLINICALTRIAL

The study Wearable-based COVID-19 Markers for Prediction of Clinical Trajectories (WAVE) is registered at https://clinicaltrials.gov (Identifier: NCT04357834). The study followed the Declaration of Helsinki, the guidelines of good clinical practice, the Swiss health laws, and the ordinance on clinical research. The study was approved by the local ethics committee Bern, Switzerland (ID 2020-00874). Each patient gave informed written consent before any study-related procedure.