Selected article for: "medical record and single center"

Author: Sun, Haoqi; Jain, Aayushee; Leone, Michael J.; Alabsi, Haitham S.; Brenner, Laura; Ye, Elissa; Ge, Wendong; Shao, Yu-Ping; Boutros, Christine; Wang, Ruopeng; Tesh, Ryan; Magdamo, Colin; Collens, Sarah I.; Ganglberger, Wolfgang; Bassett, Ingrid V.; Meigs, James B.; Kalpathy-Cramer, Jayashree; Li, Matthew D.; Chu, Jacqueline; Dougan, Michael L.; Stratton, Lawrence; Rosand, Jonathan; Fischl, Bruce; Das, Sudeshna; Mukerji, Shibani; Robbins, Gregory K.; Westover, M. Brandon
Title: COVID-19 Outpatient Screening: a Prediction Score for Adverse Events
  • Cord-id: oafxk5y2
  • Document date: 2020_6_22
  • ID: oafxk5y2
    Snippet: 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 h
    Document: 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 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.

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