The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4–88.7] and 90.8% [90.8–90.8]) and discrimination (95.1% [95.1–95.2] and 86.8% [86.8–86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.
【저자키워드】 viral infection, Health care, Prognosis, 【초록키워드】 Hospitalization, COVID-19 pandemic, outcome, sensitivity, Positive predictive value, implementation, Patient, vital signs, prognostic, retrospective, EHR, Precision, clinician, average, oxygen support, identify, develop, lab values, variables, 【제목키워드】 outcome, hospitalized COVID-19 patient,