JMIR Publications

ABSTRACT

Background:

During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of the SARS CO-V2 disease, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders in this space to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets.

Objective:

To this end, we seek to develop a COVID-19 biomarker that could provide early detection of a patient’s physiologic decompensation. As a contributing spoke in this model, we propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed.

Methods:

This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study.

Results:

Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between subjects decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 – β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored.

Conclusions:

Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT NCT04575532