Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

Abstract Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of...

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Autores principales:	Elza Rechtman, Paul Curtin, Esmeralda Navarro, Sharon Nirenberg, Megan K. Horton
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/913b829d991644c5bc84f6e1d6c34b4f
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spelling	oai:doaj.org-article:913b829d991644c5bc84f6e1d6c34b4f2021-12-02T12:33:14ZVital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system10.1038/s41598-020-78392-12045-2322https://doaj.org/article/913b829d991644c5bc84f6e1d6c34b4f2020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78392-1https://doaj.org/toc/2045-2322Abstract Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.Elza RechtmanPaul CurtinEsmeralda NavarroSharon NirenbergMegan K. HortonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-6 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elza Rechtman Paul Curtin Esmeralda Navarro Sharon Nirenberg Megan K. Horton Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
description	Abstract Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk factors for disease outcomes. Our objective was to identify characteristics available immediately upon first clinical evaluation related COVID-19 mortality. We conducted a retrospective study of 8770 laboratory-confirmed cases of SARS-CoV-2 from a network of 53 facilities in New-York City. We analysed 3 classes of variables; demographic, clinical, and comorbid factors, in a two-tiered analysis that included traditional regression strategies and machine learning. COVID-19 mortality was 12.7%. Logistic regression identified older age (OR, 1.69 [95% CI 1.66–1.92]), male sex (OR, 1.57 [95% CI 1.30–1.90]), higher BMI (OR, 1.03 [95% CI 1.102–1.05]), higher heart rate (OR, 1.01 [95% CI 1.00–1.01]), higher respiratory rate (OR, 1.05 [95% CI 1.03–1.07]), lower oxygen saturation (OR, 0.94 [95% CI 0.93–0.96]), and chronic kidney disease (OR, 1.53 [95% CI 1.20–1.95]) were associated with COVID-19 mortality. Using gradient-boosting machine learning, these factors predicted COVID-19 related mortality (AUC = 0.86) following cross-validation in a training set. Immediate, objective and culturally generalizable measures accessible upon clinical presentation are effective predictors of COVID-19 outcome. These findings may inform rapid response strategies to optimize health care delivery in parts of the world who have not yet confronted this epidemic, as well as in those forecasting a possible second outbreak.
format	article
author	Elza Rechtman Paul Curtin Esmeralda Navarro Sharon Nirenberg Megan K. Horton
author_facet	Elza Rechtman Paul Curtin Esmeralda Navarro Sharon Nirenberg Megan K. Horton
author_sort	Elza Rechtman
title	Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
title_short	Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
title_full	Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
title_fullStr	Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
title_full_unstemmed	Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system
title_sort	vital signs assessed in initial clinical encounters predict covid-19 mortality in an nyc hospital system
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/913b829d991644c5bc84f6e1d6c34b4f
work_keys_str_mv	AT elzarechtman vitalsignsassessedininitialclinicalencounterspredictcovid19mortalityinannychospitalsystem AT paulcurtin vitalsignsassessedininitialclinicalencounterspredictcovid19mortalityinannychospitalsystem AT esmeraldanavarro vitalsignsassessedininitialclinicalencounterspredictcovid19mortalityinannychospitalsystem AT sharonnirenberg vitalsignsassessedininitialclinicalencounterspredictcovid19mortalityinannychospitalsystem AT megankhorton vitalsignsassessedininitialclinicalencounterspredictcovid19mortalityinannychospitalsystem
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Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system

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