Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

BackgroundDuring the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders...

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Main Authors: Larimer, Karen, Wegerich, Stephan, Splan, Joel, Chestek, David, Prendergast, Heather, Vanden Hoek, Terry
Format: Article
Language:English
Published: JMIR Publications 2021-05-01
Series:JMIR Research Protocols
Online Access:https://www.researchprotocols.org/2021/5/e27271
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spelling doaj-dce0ef61274c4bebab7b6bff2491ea242021-05-26T12:32:36ZengJMIR PublicationsJMIR Research Protocols1929-07482021-05-01105e2727110.2196/27271Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation IndexLarimer, KarenWegerich, StephanSplan, JoelChestek, DavidPrendergast, HeatherVanden Hoek, Terry BackgroundDuring the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets. ObjectiveTo this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed. MethodsThis is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health 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 the 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. ResultsOur target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1–β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time—time to predict decompensation—and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale. ConclusionsUsing machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19. Trial RegistrationClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532 International Registered Report Identifier (IRRID)DERR1-10.2196/27271https://www.researchprotocols.org/2021/5/e27271
collection DOAJ
language English
format Article
sources DOAJ
author Larimer, Karen
Wegerich, Stephan
Splan, Joel
Chestek, David
Prendergast, Heather
Vanden Hoek, Terry
spellingShingle Larimer, Karen
Wegerich, Stephan
Splan, Joel
Chestek, David
Prendergast, Heather
Vanden Hoek, Terry
Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
JMIR Research Protocols
author_facet Larimer, Karen
Wegerich, Stephan
Splan, Joel
Chestek, David
Prendergast, Heather
Vanden Hoek, Terry
author_sort Larimer, Karen
title Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
title_short Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
title_full Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
title_fullStr Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
title_full_unstemmed Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
title_sort personalized analytics and a wearable biosensor platform for early detection of covid-19 decompensation (decode): protocol for the development of the covid-19 decompensation index
publisher JMIR Publications
series JMIR Research Protocols
issn 1929-0748
publishDate 2021-05-01
description BackgroundDuring the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets. ObjectiveTo this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed. MethodsThis is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health 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 the 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. ResultsOur target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1–β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time—time to predict decompensation—and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale. ConclusionsUsing machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19. Trial RegistrationClinicalTrials.gov NCT04575532; https://www.clinicaltrials.gov/ct2/show/NCT04575532 International Registered Report Identifier (IRRID)DERR1-10.2196/27271
url https://www.researchprotocols.org/2021/5/e27271
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