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CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis
BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpati...
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/PMC7665643/ https://www.ncbi.nlm.nih.gov/pubmed/33098643 http://dx.doi.org/10.1093/infdis/jiaa663 |
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author | Sun, Haoqi Jain, Aayushee Leone, Michael J Alabsi, Haitham S Brenner, Laura N Ye, Elissa Ge, Wendong Shao, Yu-Ping Boutros, Christine L Wang, Ruopeng Tesh, Ryan A Magdamo, Colin Collens, Sarah I Ganglberger, Wolfgang Bassett, Ingrid V Meigs, James B Kalpathy-Cramer, Jayashree Li, Matthew D Chu, Jacqueline T Dougan, Michael L Stratton, Lawrence W Rosand, Jonathan Fischl, Bruce Das, Sudeshna Mukerji, Shibani S Robbins, Gregory K Westover, M Brandon |
author_facet | Sun, Haoqi Jain, Aayushee Leone, Michael J Alabsi, Haitham S Brenner, Laura N Ye, Elissa Ge, Wendong Shao, Yu-Ping Boutros, Christine L Wang, Ruopeng Tesh, Ryan A Magdamo, Colin Collens, Sarah I Ganglberger, Wolfgang Bassett, Ingrid V Meigs, James B Kalpathy-Cramer, Jayashree Li, Matthew D Chu, Jacqueline T Dougan, Michael L Stratton, Lawrence W Rosand, Jonathan Fischl, Bruce Das, Sudeshna Mukerji, Shibani S Robbins, Gregory K Westover, M Brandon |
author_sort | Sun, Haoqi |
collection | PubMed |
description | BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March–2 May) and prospective (n = 2205, 3–14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS: In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection. |
format | Online Article Text |
id | pubmed-7665643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-76656432020-11-16 CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis Sun, Haoqi Jain, Aayushee Leone, Michael J Alabsi, Haitham S Brenner, Laura N Ye, Elissa Ge, Wendong Shao, Yu-Ping Boutros, Christine L Wang, Ruopeng Tesh, Ryan A Magdamo, Colin Collens, Sarah I Ganglberger, Wolfgang Bassett, Ingrid V Meigs, James B Kalpathy-Cramer, Jayashree Li, Matthew D Chu, Jacqueline T Dougan, Michael L Stratton, Lawrence W Rosand, Jonathan Fischl, Bruce Das, Sudeshna Mukerji, Shibani S Robbins, Gregory K Westover, M Brandon J Infect Dis Major Articles and Brief Reports BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March–2 May) and prospective (n = 2205, 3–14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS: In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection. Oxford University Press 2020-10-24 /pmc/articles/PMC7665643/ /pubmed/33098643 http://dx.doi.org/10.1093/infdis/jiaa663 Text en © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) |
spellingShingle | Major Articles and Brief Reports Sun, Haoqi Jain, Aayushee Leone, Michael J Alabsi, Haitham S Brenner, Laura N Ye, Elissa Ge, Wendong Shao, Yu-Ping Boutros, Christine L Wang, Ruopeng Tesh, Ryan A Magdamo, Colin Collens, Sarah I Ganglberger, Wolfgang Bassett, Ingrid V Meigs, James B Kalpathy-Cramer, Jayashree Li, Matthew D Chu, Jacqueline T Dougan, Michael L Stratton, Lawrence W Rosand, Jonathan Fischl, Bruce Das, Sudeshna Mukerji, Shibani S Robbins, Gregory K Westover, M Brandon CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title | CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title_full | CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title_fullStr | CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title_full_unstemmed | CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title_short | CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis |
title_sort | cova: an acuity score for outpatient screening that predicts coronavirus disease 2019 prognosis |
topic | Major Articles and Brief Reports |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665643/ https://www.ncbi.nlm.nih.gov/pubmed/33098643 http://dx.doi.org/10.1093/infdis/jiaa663 |
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