A Prognostic Model for Death in COVID-19 Patients Presenting to the Emergency Room: The Added Value of Computed Tomography.

Abstract Objective: The added value of CT in prognostic models for coronavirus disease 2019 (COVID-19) patients is unclear. The aim of this study was to develop a prognostic model for death in COVID-19 patients using clinical and CT variables.Methods: Consecutive patients who presented to the emergency room between February 27 and March 23, 2020 for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20-39%, 40-59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (CvAUC).Results: Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level (SO2). CT disease extension was also independently associated with death (HR=7.56, 95% CI=3.49; 16.38 for ≥ 60% extension). CvAUCs were 0.927 (bootstrap bias corrected-95%CI=0.899-0.947) for the clinical model and 0.936 (bootstrap bias corrected-95%CI=0.912-0.953) when adding CT extension.Conclusions: A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy.