Early detection of SARS-CoV-2 and other infections in solid organ transplant recipients and household members using wearable devices

Abstract

The increasing global prevalence of SARS-CoV-2 and the resulting COVID-19 disease pandemic pose significant concerns for clinical management of solid organ transplant recipients (SOTR). Wearable devices that can measure physiologic changes in biometrics including heart rate, heart rate variability, body temperature, respiratory, activity (such as steps taken per day) and sleep patterns, and blood oxygen saturation show utility for the early detection of infection before clinical presentation of symptoms. Recent algorithms developed using preliminary wearable datasets show that SARS-CoV-2 is detectable before clinical symptoms in >80% of adults. Early detection of SARS-CoV-2, influenza, and other pathogens in SOTR, and their household members, could facilitate early interventions such as self-isolation and early clinical management of relevant infection(s). Ongoing studies testing the utility of wearable devices such as smartwatches for early detection of SARS-CoV-2 and other infections in the general population are reviewed here, along with the practical challenges to implementing these processes at scale in pediatric and adult SOTR, and their household members. The resources and logistics, including transplant-specific analyses pipelines to account for confounders such as polypharmacy and comorbidities, required in studies of pediatric and adult SOTR for the robust early detection of SARS-CoV-2, and other infections are also reviewed.

Keywords: eHealth; mHealth; telemedicine; transplantation; wearables.

Figure 1

Algorithmic analyses of wearable device biometric datasets from a single individual pre‐, peri‐, and post‐SARS‐CoV‐2 infection. The patient’s HR, activity steps, and sleep record were collected over all of February and March 2020, which encompassed pre‐, peri‐, and post‐SARS‐CoV‐2 infection. The average resting HR from healthy baseline days in February was compared to the average from all days in March 2020 (test days). The date (in red) indicate the day the patient reported initial symptoms and the subsequent day (in purple) shows the date of formal SARS‐CoV‐2 diagnoses by RT‐PCR. Periods around SARS‐CoV‐2 infection correlated with heart rates (HR) that were significantly increased above the baseline HR. The Resting Heart‐Rate‐Difference detection method (RHR‐Diff) was used to systematically identify periods of elevated HR based on outlier interval detection, and compared a normal baseline to each HR observation to calculate standardized residuals. Panel 1a shows the RHR‐Diff elevated time intervals (red arrowed horizontal line), identifying a 10‐day window of significant HR elevation before the onset of reported symptoms. Online detection results based on the number of successive outlier hours (panel b) and the CuSum continuous real‐time alerts (panel c). Individuals for this study were recruited with appropriate informed consent under protocol number 55577 approved by the Stanford University Institutional Review Board. The dates shown were staggered by +/‐ 7 days to protect study participant’s identities.

Figure 2

Monitoring of transplant recipients and their family members for early detection of infection. The data collected from wearables on transplant recipients and their families are monitored by a clinical team. Robust abnormal deviations of key physiological biometric baseline signals may indicate potential infection which can be verified through clinical/telehealth consults or measured using orthogonal devices. The algorithms sensitivities can be adjusted to reduce false negatives for confounding factors such as medications impacting HR and ambulatory BP. Confirmed sustained biometric abnormalities can instigate preventative self‐isolation of potentially infected household members and instigation of formal diagnoses of the infection(s). Anticipated triggering of recipients, and any telemedicine/other investigative care such as at‐home SARS‐CoV‐2 clinical testing, can be performed through defined protocols from the local clinical care team. Data protection includes no personal health information (PHI) transfer and limiting the activity data so that no geolocation data are recorded.