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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.
  • Sun H; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Jain A; Harvard Medical School, Boston, Massachusetts, USA.
  • Leone MJ; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Alabsi HS; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Brenner LN; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Ye E; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Ge W; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Shao YP; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Boutros CL; Harvard Medical School, Boston, Massachusetts, USA.
  • Wang R; Harvard Medical School, Boston, Massachusetts, USA.
  • Tesh RA; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Magdamo C; Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Collens SI; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Ganglberger W; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Bassett IV; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Meigs JB; Harvard Medical School, Boston, Massachusetts, USA.
  • Kalpathy-Cramer J; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Li MD; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Chu JT; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Dougan ML; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Stratton LW; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Rosand J; Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA.
  • Fischl B; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Das S; Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Mukerji SS; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Robbins GK; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Westover MB; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
J Infect Dis ; 223(1): 38-46, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1066343
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Infect Dis Year: 2021 Document Type: Article Affiliation country: Infdis

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Severity of Illness Index / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: J Infect Dis Year: 2021 Document Type: Article Affiliation country: Infdis