Insights from a Rapidly Implemented COVID-19 Biobank Using Electronic Consent and Informatics Tools.

Biobanking during the COVID-19 pandemic presented unique challenges regarding patient enrollment, sample collection, and experimental analysis. This report details the ways in which we rapidly overcame those challenges to create a robust database of clinical information and patient samples while maintaining clinician and researcher safety. We developed a pipeline using REDCap (Research Electronic Data Capture) to coordinate electronic informed consent, sample collection, immunological assay execution, and data analysis for biobanking samples from patients with COVID-19. We then integrated immunological assay data with clinical data extracted from the electronic health record to link study parameters with clinical readouts. Of the 193 inpatients who participated in this study, 138 consented electronically and 56 provided paper consent. We collected and banked blood samples to measure circulating cytokines and chemokines, peripheral immune cell composition and activation status, anti-COVID-19 antibodies, and germline gene polymorphisms. In addition, we collected DNA and RNA from nasopharyngeal swabs to assess viral titer and microbiome composition by 16S sequencing. The rapid spread and contagious nature of COVID-19 required special considerations and innovative solutions to biobank samples quickly while protecting researchers and clinicians. Overall, this workflow and computational pipeline allowed for comprehensive immune profiling of 193 inpatients infected with COVID-19, as well as 89 outpatients, 157 patients receiving curbside COVID-19 testing, and 86 healthy controls. We describe a novel electronic framework for biobanking and analyzing patient samples during COVID-19, and present insights and strategies that can be applied more broadly to other biobank studies.

Real-time alerting system for COVID-19 and other stress events using wearable data.

Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

A scalable, secure, and interoperable platform for deep data-driven health management.

The large amount of biomedical data derived from wearable sensors, electronic health records, and molecular profiling (e.g., genomics data) is rapidly transforming our healthcare systems. The increasing scale and scope of biomedical data not only is generating enormous opportunities for improving health outcomes but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, we developed the Personal Health Dashboard (PHD), which utilizes state-of-the-art security and scalability technologies to provide an end-to-end solution for big biomedical data analytics. The PHD platform is an open-source software framework that can be easily configured and deployed to any big data health project to store, organize, and process complex biomedical data sets, support real-time data analysis at both the individual level and the cohort level, and ensure participant privacy at every step. In addition to presenting the system, we illustrate the use of the PHD framework for large-scale applications in emerging multi-omics disease studies, such as collecting and visualization of diverse data types (wearable, clinical, omics) at a personal level, investigation of insulin resistance, and an infrastructure for the detection of presymptomatic COVID-19.

Adapting skills from genetic counseling to wearables technology research during the COVID-19 pandemic: Poised for the pivot.

Genetic counselors have shown themselves to be adaptable in an evolving profession, with expansion into new sub-specialties, various non-clinical settings, and research roles. The COVID-19 pandemic caused a sudden and drastic shift in healthcare priorities. In an effort to contribute meaningfully to the COVID-19 crisis, and to adapt to a remote- and essential-only research environment, our workplace and thus our roles pivoted from genomics research to remote COVID-19 research using wearables technologies. With a deep understanding of genomic data, we were quickly able to apply similar concepts to wearables data including considering privacy implications, managing uncertain findings, and acknowledging the lack of ethnic diversity in many datasets. By sharing our own experience as an example, we hope individuals trained in genetic counseling may see opportunities for adaptation of their skills into expanding roles.

COVIDOSE: A Phase II Clinical Trial of Low-Dose Tocilizumab in the Treatment of Noncritical COVID-19 Pneumonia.

Interleukin-6 (IL-6)-mediated hyperinflammation may contribute to the mortality of coronavirus disease 2019 (COVID-19). The IL-6 receptor-blocking monoclonal antibody tocilizumab has been repurposed for COVID-19, but prospective trials and dose-finding studies in COVID-19 have not yet fully reported. We conducted a single-arm phase II trial of low-dose tocilizumab in nonintubated hospitalized adult patients with COVID-19, radiographic pulmonary infiltrate, fever, and C-reactive protein (CRP) ≥ 40 mg/L. We hypothesized that doses significantly lower than the emerging standards of 400 mg or 8 mg/kg would resolve clinical and laboratory indicators of hyperinflammation. A dose range from 40 to 200 mg was evaluated, with allowance for one repeat dose at 24 to 48 hours. The primary objective was to assess the relationship of dose to fever resolution and CRP response. Thirty-two patients received low-dose tocilizumab, with the majority experiencing fever resolution (75%) and CRP decline consistent with IL-6 pathway abrogation (86%) in the 24-48 hours following drug administration. There was no evidence of a relationship between dose and fever resolution or CRP decline over the dose range of 40-200 mg. Within the 28-day follow-up, 5 (16%) patients died. For patients who recovered, median time to clinical recovery was 3 days (interquartile range, 2-5). Clinically presumed and/or cultured bacterial superinfections were reported in 5 (16%) patients. Low-dose tocilizumab was associated with rapid improvement in clinical and laboratory measures of hyperinflammation in hospitalized patients with COVID-19. Results of this trial provide rationale for a randomized, controlled trial of low-dose tocilizumab in COVID-19.

Pre-symptomatic detection of COVID-19 from smartwatch data.

Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.