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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...
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/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. |
format | Online Article Text |
id | pubmed-7886884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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