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Real-time prediction of COVID-19 related mortality using electronic health records

Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enabl...

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Autores principales: Schwab, Patrick, Mehrjou, Arash, Parbhoo, Sonali, Celi, Leo Anthony, Hetzel, Jürgen, Hofer, Markus, Schölkopf, Bernhard, Bauer, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886884/
https://www.ncbi.nlm.nih.gov/pubmed/33594046
http://dx.doi.org/10.1038/s41467-020-20816-7
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author Schwab, Patrick
Mehrjou, Arash
Parbhoo, Sonali
Celi, Leo Anthony
Hetzel, Jürgen
Hofer, Markus
Schölkopf, Bernhard
Bauer, Stefan
author_facet Schwab, Patrick
Mehrjou, Arash
Parbhoo, Sonali
Celi, Leo Anthony
Hetzel, Jürgen
Hofer, Markus
Schölkopf, Bernhard
Bauer, Stefan
author_sort Schwab, Patrick
collection PubMed
description Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality.
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spelling pubmed-78868842021-03-03 Real-time prediction of COVID-19 related mortality using electronic health records Schwab, Patrick Mehrjou, Arash Parbhoo, Sonali Celi, Leo Anthony Hetzel, Jürgen Hofer, Markus Schölkopf, Bernhard Bauer, Stefan Nat Commun Article Coronavirus disease 2019 (COVID-19) is a respiratory disease with rapid human-to-human transmission caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the exponential growth of infections, identifying patients with the highest mortality risk early is critical to enable effective intervention and prioritisation of care. Here, we present the COVID-19 early warning system (CovEWS), a risk scoring system for assessing COVID-19 related mortality risk that we developed using data amounting to a total of over 2863 years of observation time from a cohort of 66 430 patients seen at over 69 healthcare institutions. On an external cohort of 5005 patients, CovEWS predicts mortality from 78.8% (95% confidence interval [CI]: 76.0, 84.7%) to 69.4% (95% CI: 57.6, 75.2%) specificity at sensitivities greater than 95% between, respectively, 1 and 192 h prior to mortality events. CovEWS could enable earlier intervention, and may therefore help in preventing or mitigating COVID-19 related mortality. Nature Publishing Group UK 2021-02-16 /pmc/articles/PMC7886884/ /pubmed/33594046 http://dx.doi.org/10.1038/s41467-020-20816-7 Text en © The Author(s) 2021 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/.
spellingShingle Article
Schwab, Patrick
Mehrjou, Arash
Parbhoo, Sonali
Celi, Leo Anthony
Hetzel, Jürgen
Hofer, Markus
Schölkopf, Bernhard
Bauer, Stefan
Real-time prediction of COVID-19 related mortality using electronic health records
title Real-time prediction of COVID-19 related mortality using electronic health records
title_full Real-time prediction of COVID-19 related mortality using electronic health records
title_fullStr Real-time prediction of COVID-19 related mortality using electronic health records
title_full_unstemmed Real-time prediction of COVID-19 related mortality using electronic health records
title_short Real-time prediction of COVID-19 related mortality using electronic health records
title_sort real-time prediction of covid-19 related mortality using electronic health records
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886884/
https://www.ncbi.nlm.nih.gov/pubmed/33594046
http://dx.doi.org/10.1038/s41467-020-20816-7
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