COVID-19 Outpatient Screening: a Prediction Score for Adverse Events
| Autor: | Wolfgang Ganglberger, Sarah I. Collens, Jacqueline T. Chu, Matthew D. Li, Gregory K. Robbins, Colin Magdamo, Bruce Fischl, Lawrence W. Stratton, Michael J. Leone, Ryan A. Tesh, Aayushee Jain, Christine L. Boutros, Shibani S. Mukerji, Ruopeng Wang, James B. Meigs, Yu-Ping Shao, M. Brandon Westover, Haoqi Sun, Wendong Ge, Haitham Alabsi, Sudeshna Das, Ingrid V. Bassett, Laura N. Brenner, Michael Dougan, Jayashree Kalpathy-Cramer, Elissa Ye, Jonathan Rosand |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male medicine.medical_specialty Respiratory rate Critical Illness Concurrent validity Sensitivity and Specificity Severity of Illness Index Article Predictive Value of Tests Internal medicine Outpatients medicine Humans Prospective Studies Prospective cohort study Adverse effect Aged Interpretable machine learning business.industry COVID-19 Outpatient Emergency department Middle Aged Models Theoretical Prognosis Hospitalization Intensive Care Units Blood pressure ROC Curve Cohort Female Ordered logit Risk Prediction business |
| Zdroj: | medRxiv article-version (status) pre article-version (number) 1 |
| DOI: | 10.1101/2020.06.17.20134262 |
| Popis: | BackgroundWe 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.MethodsSingle-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 ratio (E/O). Discrimination was assessed by C-statistics (AUC).ResultsIn 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.ConclusionsCoVA is a prospectively validated automatable score to assessing risk for adverse outcomes related to COVID-19 infection in the outpatient setting. |
| Databáze: | OpenAIRE |
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