Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

Abstract The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of var...

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Autores principales:	Dylan M. Richards, MacKenzie J. Tweardy, Steven R. Steinhubl, David W. Chestek, Terry L. Vanden Hoek, Karen A. Larimer, Stephan W. Wegerich
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/2d633c5dcb0849869d647f1547a21803
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spelling	oai:doaj.org-article:2d633c5dcb0849869d647f1547a218032021-11-14T12:08:54ZWearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients10.1038/s41746-021-00527-z2398-6352https://doaj.org/article/2d633c5dcb0849869d647f1547a218032021-11-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00527-zhttps://doaj.org/toc/2398-6352Abstract The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.Dylan M. RichardsMacKenzie J. TweardySteven R. SteinhublDavid W. ChestekTerry L. Vanden HoekKaren A. LarimerStephan W. WegerichNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Dylan M. Richards MacKenzie J. Tweardy Steven R. Steinhubl David W. Chestek Terry L. Vanden Hoek Karen A. Larimer Stephan W. Wegerich Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
description	Abstract The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.
format	article
author	Dylan M. Richards MacKenzie J. Tweardy Steven R. Steinhubl David W. Chestek Terry L. Vanden Hoek Karen A. Larimer Stephan W. Wegerich
author_facet	Dylan M. Richards MacKenzie J. Tweardy Steven R. Steinhubl David W. Chestek Terry L. Vanden Hoek Karen A. Larimer Stephan W. Wegerich
author_sort	Dylan M. Richards
title	Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
title_short	Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
title_full	Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
title_fullStr	Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
title_full_unstemmed	Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
title_sort	wearable sensor derived decompensation index for continuous remote monitoring of covid-19 diagnosed patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2d633c5dcb0849869d647f1547a21803
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Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

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