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362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients
BACKGROUND: The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777853/ http://dx.doi.org/10.1093/ofid/ofaa439.557 |
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author | Cavalier, Joanna S Goldstein, Benjamin O’Brien, Cara L Bedoya, Armando |
author_facet | Cavalier, Joanna S Goldstein, Benjamin O’Brien, Cara L Bedoya, Armando |
author_sort | Cavalier, Joanna S |
collection | PubMed |
description | BACKGROUND: The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1.4% died. Several models have been developed to assess decompensating patients. However, research examining their applicability to COVID-19 patients is limited. An accurate predictive model for patients at risk of decompensation is critical for health systems to optimally triage emergencies, care for patients, and allocate resources. METHODS: An early warning score (EWS) algorithm created within a large academic medical center, with methodology previously described, was applied to COVID-19 patients admitted to this institution. 122 COVID-19 patients were included. A decompensation event was defined as inpatient mortality or an unanticipated transfer to an ICU from an intermediate medical ward. The EWS was calculated at 12-hour and 24-hour intervals. RESULTS: Of 122 patients admitted with COVID-19, 28 had a decompensation event, yielding an event rate of 23.0%. 8 patients died, 13 transferred to the ICU, and 6 both transferred to the ICU and died. Decompensation within 12 and 24 hours were predicted with areas under the curve (AUC) of 0.850 and 0.817, respectively. Using a three-tiered risk model, use of the customized EWS score for patients identified as high risk of decompensation had a positive predictive value of 44.4% and 11.1% and specificity of 99.3% and 99.6% and 12- and 24-hour intervals. Amongst medium-risk patients, the score had a specificity of 85.0% and 85.4%, respectively. CONCLUSION: This EWS allows for prediction of decompensation, defined as transfer to an ICU or death, in COVID-19 patients with excellent specificity and a high positive predictive value. Clinically, implementation of this score can help to identify patients before they decompensate in order to triage at time of presentation and allocate step-down beds, ICU beds, and treatments such as remdesivir. DISCLOSURES: All Authors: No reported disclosures |
format | Online Article Text |
id | pubmed-7777853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-77778532021-01-07 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients Cavalier, Joanna S Goldstein, Benjamin O’Brien, Cara L Bedoya, Armando Open Forum Infect Dis Poster Abstracts BACKGROUND: The novel coronavirus disease (COVID-19) results in severe illness in a significant proportion of patients, necessitating a way to discern which patients will become critically ill and which will not. In one large case series, 5.0% of patients required an intensive care unit (ICU) and 1.4% died. Several models have been developed to assess decompensating patients. However, research examining their applicability to COVID-19 patients is limited. An accurate predictive model for patients at risk of decompensation is critical for health systems to optimally triage emergencies, care for patients, and allocate resources. METHODS: An early warning score (EWS) algorithm created within a large academic medical center, with methodology previously described, was applied to COVID-19 patients admitted to this institution. 122 COVID-19 patients were included. A decompensation event was defined as inpatient mortality or an unanticipated transfer to an ICU from an intermediate medical ward. The EWS was calculated at 12-hour and 24-hour intervals. RESULTS: Of 122 patients admitted with COVID-19, 28 had a decompensation event, yielding an event rate of 23.0%. 8 patients died, 13 transferred to the ICU, and 6 both transferred to the ICU and died. Decompensation within 12 and 24 hours were predicted with areas under the curve (AUC) of 0.850 and 0.817, respectively. Using a three-tiered risk model, use of the customized EWS score for patients identified as high risk of decompensation had a positive predictive value of 44.4% and 11.1% and specificity of 99.3% and 99.6% and 12- and 24-hour intervals. Amongst medium-risk patients, the score had a specificity of 85.0% and 85.4%, respectively. CONCLUSION: This EWS allows for prediction of decompensation, defined as transfer to an ICU or death, in COVID-19 patients with excellent specificity and a high positive predictive value. Clinically, implementation of this score can help to identify patients before they decompensate in order to triage at time of presentation and allocate step-down beds, ICU beds, and treatments such as remdesivir. DISCLOSURES: All Authors: No reported disclosures Oxford University Press 2020-12-31 /pmc/articles/PMC7777853/ http://dx.doi.org/10.1093/ofid/ofaa439.557 Text en © The Author 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Poster Abstracts Cavalier, Joanna S Goldstein, Benjamin O’Brien, Cara L Bedoya, Armando 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title | 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title_full | 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title_fullStr | 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title_full_unstemmed | 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title_short | 362. A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients |
title_sort | 362. a modified early warning score predicts decompensation in covid-19 patients |
topic | Poster Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777853/ http://dx.doi.org/10.1093/ofid/ofaa439.557 |
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