Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

Abstract Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common com...

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Autores principales:	Viktor Tóth, Marsha Meytlis, Douglas P. Barnaby, Kevin R. Bock, Michael I. Oppenheim, Yousef Al-Abed, Thomas McGinn, Karina W. Davidson, Lance B. Becker, Jamie S. Hirsch, Theodoros P. Zanos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/588ee960f8614f5ca0c8f6d97b642545
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spelling	oai:doaj.org-article:588ee960f8614f5ca0c8f6d97b6425452021-12-02T11:02:17ZLet Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model10.1038/s41746-020-00355-72398-6352https://doaj.org/article/588ee960f8614f5ca0c8f6d97b6425452020-11-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00355-7https://doaj.org/toc/2398-6352Abstract Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.Viktor TóthMarsha MeytlisDouglas P. BarnabyKevin R. BockMichael I. OppenheimYousef Al-AbedThomas McGinnKarina W. DavidsonLance B. BeckerJamie S. HirschTheodoros P. ZanosNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
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 Viktor Tóth Marsha Meytlis Douglas P. Barnaby Kevin R. Bock Michael I. Oppenheim Yousef Al-Abed Thomas McGinn Karina W. Davidson Lance B. Becker Jamie S. Hirsch Theodoros P. Zanos Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
description	Abstract Impaired sleep for hospital patients is an all too common reality. Sleep disruptions due to unnecessary overnight vital sign monitoring are associated with delirium, cognitive impairment, weakened immunity, hypertension, increased stress, and mortality. It is also one of the most common complaints of hospital patients while imposing additional burdens on healthcare providers. Previous efforts to forgo overnight vital sign measurements and improve patient sleep used providers’ subjective stability assessment or utilized an expanded, thus harder to retrieve, set of vitals and laboratory results to predict overnight clinical risk. Here, we present a model that incorporates past values of a small set of vital signs and predicts overnight stability for any given patient-night. Using data obtained from a multi-hospital health system between 2012 and 2019, a recurrent deep neural network was trained and evaluated using ~2.3 million admissions and 26 million vital sign assessments. The algorithm is agnostic to patient location, condition, and demographics, and relies only on sequences of five vital sign measurements, a calculated Modified Early Warning Score, and patient age. We achieved an area under the receiver operating characteristic curve of 0.966 (95% confidence interval [CI] 0.956–0.967) on the retrospective testing set, and 0.971 (95% CI 0.965–0.974) on the prospective set to predict overnight patient stability. The model enables safe avoidance of overnight monitoring for ~50% of patient-nights, while only misclassifying 2 out of 10,000 patient-nights as stable. Our approach is straightforward to deploy, only requires regularly obtained vital signs, and delivers easily actionable clinical predictions for a peaceful sleep in hospitals.
format	article
author	Viktor Tóth Marsha Meytlis Douglas P. Barnaby Kevin R. Bock Michael I. Oppenheim Yousef Al-Abed Thomas McGinn Karina W. Davidson Lance B. Becker Jamie S. Hirsch Theodoros P. Zanos
author_facet	Viktor Tóth Marsha Meytlis Douglas P. Barnaby Kevin R. Bock Michael I. Oppenheim Yousef Al-Abed Thomas McGinn Karina W. Davidson Lance B. Becker Jamie S. Hirsch Theodoros P. Zanos
author_sort	Viktor Tóth
title	Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
title_short	Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
title_full	Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
title_fullStr	Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
title_full_unstemmed	Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
title_sort	let sleeping patients lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/588ee960f8614f5ca0c8f6d97b642545
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Let Sleeping Patients Lie, avoiding unnecessary overnight vitals monitoring using a clinically based deep-learning model

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