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Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients
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 pers...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576003/ https://www.ncbi.nlm.nih.gov/pubmed/34750499 http://dx.doi.org/10.1038/s41746-021-00527-z |
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author | Richards, Dylan M. Tweardy, MacKenzie J. Steinhubl, Steven R. Chestek, David W. Hoek, Terry L. Vanden Larimer, Karen A. Wegerich, Stephan W. |
author_facet | Richards, Dylan M. Tweardy, MacKenzie J. Steinhubl, Steven R. Chestek, David W. Hoek, Terry L. Vanden Larimer, Karen A. Wegerich, Stephan W. |
author_sort | Richards, Dylan M. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8576003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85760032021-11-19 Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients Richards, Dylan M. Tweardy, MacKenzie J. Steinhubl, Steven R. Chestek, David W. Hoek, Terry L. Vanden Larimer, Karen A. Wegerich, Stephan W. NPJ Digit Med Article 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. Nature Publishing Group UK 2021-11-08 /pmc/articles/PMC8576003/ /pubmed/34750499 http://dx.doi.org/10.1038/s41746-021-00527-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Richards, Dylan M. Tweardy, MacKenzie J. Steinhubl, Steven R. Chestek, David W. Hoek, Terry L. Vanden Larimer, Karen A. Wegerich, Stephan W. Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576003/ https://www.ncbi.nlm.nih.gov/pubmed/34750499 http://dx.doi.org/10.1038/s41746-021-00527-z |
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