| Abstract: | Objectives: The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones.Methods: Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hos pitals were included. Logistic regression analysis was performed to develop a prediction model for in hospital mortality, based on the 3978 patients admitted between March–July, 2020. The model was validated in the 1054 patients admitted during August–September, as well as in an external cohort of 474 Spanish patients.Results: Median (25–75th percentile) age of the model-derivation cohort was 60 (48–72) years, and in hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mor tality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO2/FiO2 ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829–0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833–0.885]) and Spanish (0.894 [95% CI 0.870–0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). Conclusions: An easy-to-use rapid scoring system based on characteristics of COVID-19 patients com monly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19. |
