How to Predict SARS-COV2 Infection in Patient Admitted in Emergency Department: A Prospective Cohort Study.

Abstract Background. In order to rapidly identifying patients with a low probability of being infected by COVID19 to quickly refer them to specialized departments, the objective of our study was to develop a clinical predictive model of infection by COVID19 in patients attending the ED for respiratory symptoms or unexplained fever.Methods. We included all patients over 15 years old, admitted in one of the 2 emergency departments of Toulouse University Hospital between March 13 and March 31 for respiratory symptoms (dyspnea, cough), or fever (or sensation of fever) of unknown origin, and potentially requiring hospitalization. COVID19 infection was assessed by CT-SCAN and RT-PCR. All the candidate predictors were variables collected during the first clinical examination. Internal validation of the final model was performed using the bootstrap procedure. We performed a temporal validation in the same way on patients included between April 1 and April 13.Results. 772 patients were included. The prevalence of COVID19 was 25.5%. There were 19 predictors in the final model. The corrected-by-optimism area under the curve was 0.86 (95%CI = [0.83;0.89]). For a threshold at 10%, the sensitivity was 92%., the specificity was 56%, and the false negative rate was 5%. In secondary data, including 387 patients, the prevalence of COVID19 was 15%. The area under the curve was 0.73 (95%CI = [0.63;0.83]). For the same threshold, the sensitivity was 78%, the specificity was 48%, and the false negative rate was 7%Conclusion. We have developed a predictive tool of COVID19 infection for patients attending the ED. It could safely reduce admission in COVID19 dedicated unit in ED and prevent its overcrowding.Trial registration number: NCT: RC31/20/0149