COVID-19 Outpatient Screening: a Prediction Score for Adverse Events.

Autor:	Sun H; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Jain A; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Leone MJ; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Alabsi HS; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA., Brenner L; Harvard Medical School, Boston, MA.; Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA., Ye E; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Ge W; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Shao YP; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Boutros C; Department of Neurology, Massachusetts General Hospital, Boston, MA., Wang R; Department of Radiology, Massachusetts General Hospital, Boston, MA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA., Tesh R; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Magdamo C; Department of Neurology, Massachusetts General Hospital, Boston, MA., Collens SI; Department of Neurology, Massachusetts General Hospital, Boston, MA., Ganglberger W; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA., Bassett IV; Harvard Medical School, Boston, MA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA., Meigs JB; Harvard Medical School, Boston, MA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA., Kalpathy-Cramer J; Harvard Medical School, Boston, MA.; Department of Radiology, Massachusetts General Hospital, Boston, MA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA., Li MD; Harvard Medical School, Boston, MA.; Department of Radiology, Massachusetts General Hospital, Boston, MA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA., Chu J; Harvard Medical School, Boston, MA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA.; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA.; MGH Chelsea HealthCare Center, Chelsea, MA., Dougan ML; Harvard Medical School, Boston, MA.; Division of Gastroenterology, Massachusetts General Hospital, Boston, MA., Stratton L; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA., Rosand J; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA., Fischl B; Harvard Medical School, Boston, MA.; Department of Radiology, Massachusetts General Hospital, Boston, MA.; Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA.; MIT HST/CSAIL, Cambridge, MA., Das S; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA., Mukerji S; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA., Robbins GK; Harvard Medical School, Boston, MA.; Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA., Westover MB; Department of Neurology, Massachusetts General Hospital, Boston, MA.; Harvard Medical School, Boston, MA.; Clinical Data AI Center (CDAC), Massachusetts General Hospital, Boston, MA.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2020 Jun 22. Date of Electronic Publication: 2020 Jun 22.
DOI:	10.1101/2020.06.17.20134262
Abstrakt:	Background: We sought to develop an automatable score to predict hospitalization, critical illness, or death in patients at risk for COVID-19 presenting for urgent care during the Massachusetts outbreak. Methods: Single-center study of adult outpatients seen in respiratory illness clinics (RICs) or the emergency department (ED), including development (n = 9381, March 7-May 2) and prospective (n = 2205, May 3-14) cohorts. Data was queried from Partners Enterprise Data Warehouse. Outcomes were hospitalization, critical illness or death within 7 days. We developed the COVID-19 Acuity Score (CoVA) using automatically extracted data from the electronic medical record and learning-to-rank ordinal logistic regression modeling. Calibration was assessed using predicted-to-observed event ratio (E/O). Discrimination was assessed by C-statistics (AUC). Results: In the development cohort, 27.3%, 7.2%, and 1.1% of patients experienced hospitalization, critical illness, or death, respectively; and in the prospective cohort, 26.1%, 6.3%, and 0.5%. CoVA showed excellent performance in the development cohort (concurrent validation) for hospitalization (E/O: 1.00, AUC: 0.80); for critical illness (E/O: 1.00, AUC: 0.82); and for death (E/O: 1.00, AUC: 0.87). Performance in the prospective cohort (prospective validation) was similar for hospitalization (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 five were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. Conclusions: CoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting.
Databáze:	MEDLINE
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