Background: Patient safety and optimal resource allocation are essential during the COVID-19 pandemic. We present a COVID-19 prognostic algorithm implemented in real-time as a clinical decision support structure for symptomatic persons under investigation (PUI) for COVID-19 in the emergency department (ED). Methods: : The training data included 1,469 patients who tested positive for SARS-CoV-2 within 14 days of acute care. The validation was done using a retrospective set of 414 SARS-CoV-2 positive patients and a PUI set of 13,271 patients who had symptomatic SARS-CoV-2 test during acute care visit. We performed a real-time model assessment on 2,174 patients with an ED visit and symptomatic test or COVID-19 positive result. The logistic regression prognostic model used demographics, comorbidities, home medications, and vital signs factors. The COVID-19 severity outcome comprised a composite of intensive care unit (ICU) admission, invasive mechanical ventilation (ventilator) use, and mortality. Results: : The area under the receiver operating characteristic (AUROC) was 0.87 (95%-CI: 0.83, 0.91) in the retrospective validation and 0.82 (95%-CI: 0.81, 0.83) in the PUI population for predicting COVID-19 severity. The model had an AUROC of 0.85 (95%-CI, 0.83, 0.87) in the real-time set. The rates of ICU admission, ventilator use, and death in patients with the lowest 20% of the scores were zero, significantly lower compared to those rates (20.6%, 9.5%, and 18.1%, respectively) for patients with the highest 20% of the scores in the real-time set. The model performed equitably across racial/ethnic minorities. Conclusion: Our prognostic model score developed on COVID-19 positive patients performed well on PUI patients and in real-time in ED to support clinical decision making.
Implementation and Evaluation of a Novel COVID-19 Prognostic Score for Patients with Suspected COVID-19