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

Author:

Sun Haoqi123ORCID,Jain Aayushee13,Leone Michael J13,Alabsi Haitham S12,Brenner Laura N245,Ye Elissa13,Ge Wendong123,Shao Yu-Ping13,Boutros Christine L1,Wang Ruopeng67,Tesh Ryan A13,Magdamo Colin1,Collens Sarah I1,Ganglberger Wolfgang13ORCID,Bassett Ingrid V28,Meigs James B25,Kalpathy-Cramer Jayashree267,Li Matthew D267,Chu Jacqueline T2589,Dougan Michael L210,Stratton Lawrence W12,Rosand Jonathan12,Fischl Bruce26711,Das Sudeshna12,Mukerji Shibani S12,Robbins Gregory K28,Westover M Brandon123

Affiliation:

1. Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA

2. Harvard Medical School, Boston, Massachusetts, USA

3. Clinical Data AI Center, Massachusetts General Hospital, Boston, Massachusetts, USA

4. Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA

5. Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA

6. Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA

7. Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, USA

8. Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, USA

9. MGH Chelsea HealthCare Center, Chelsea, Massachusetts, USA

10. Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA

11. Massachusetts Institute of Technology Health Sciences & Technology Program/Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA

Abstract

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.

Funder

National Institutes of Health

National Institute of Allergy and Infectious Diseases

National Institute of Biomedical Imaging and Bioengineering

National Institute on Aging

National Institute of Diabetes and Digestive and Kidney Diseases

National Institute of Neurological Disorders and Stroke

NIH Blueprint for Neuroscience Research

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Immunology and Allergy

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