CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis.

Autor:	Sun H; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Jain A; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Leone MJ; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Alabsi HS; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA., Brenner LN; Harvard Medical School, Boston, Massachusetts, USA.; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA., Ye E; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Ge W; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Shao YP; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Boutros CL; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA., Wang R; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA., Tesh RA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Magdamo C; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA., Collens SI; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA., Ganglberger W; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA., Bassett IV; Harvard Medical School, Boston, Massachusetts, USA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA., Meigs JB; Harvard Medical School, Boston, Massachusetts, USA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA., Kalpathy-Cramer J; Harvard Medical School, Boston, Massachusetts, USA.; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA., Li MD; Harvard Medical School, Boston, Massachusetts, USA.; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA., Chu JT; Harvard Medical School, Boston, Massachusetts, USA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA.; MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA., Dougan ML; Harvard Medical School, Boston, Massachusetts, USA.; Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA., Stratton LW; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA., Rosand J; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA., Fischl B; Harvard Medical School, Boston, Massachusetts, USA.; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.; Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA., Das S; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA., Mukerji SS; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA., Robbins GK; Harvard Medical School, Boston, Massachusetts, USA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA., Westover MB; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.; Harvard Medical School, Boston, Massachusetts, USA.; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
Jazyk:	angličtina
Zdroj:	The Journal of infectious diseases [J Infect Dis] 2021 Jan 04; Vol. 223 (1), pp. 38-46.
DOI:	10.1093/infdis/jiaa663
Abstrakt:	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. (© 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.)
Databáze:	MEDLINE
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