Risk Score for Predicting In-Hospital Mortality in COVID-19 (RIM Score)
Alejandro López‐Escobar, Rodrigo Madurga, José M. Castellano, Sara Velázquez, R. Suárez del Villar, Justo Menéndez, Alejandro Peixoto, Sara Jimeno, Paula Sol Ventura, Santiago Ruiz de Aguiar
Abstract
Infection by SARS-CoV2 has devastating consequences on health care systems. It is a global health priority to identify patients at risk of fatal outcomes. 1955 patients admitted to HM-Hospitales from 1 March to 10 June 2020 due to COVID-19, were were divided into two groups, 1310 belonged to the training cohort and 645 to validation cohort. Four different models were generated to predict in-hospital mortality. Following variables were included: age, sex, oxygen saturation, level of C-reactive-protein, neutrophil-to-platelet-ratio (NPR), neutrophil-to-lymphocyte-ratio (NLR) and the rate of changes of both hemogram ratios (VNLR and VNPR) during the first week after admission. The accuracy of the models in predicting in-hospital mortality were evaluated using the area under the receiver-operator-characteristic curve (AUC). AUC for models including NLR and NPR performed similarly in both cohorts: NLR 0.873 (95% CI: 0.849-0.898), NPR 0.875 (95% CI: 0.851-0.899) in training cohort and NLR 0.856 (95% CI: 0.818-0.895), NPR 0.863 (95% CI: 0.826-0.901) in validation cohort. AUC was 0.885 (95% CI: 0.885-0.919) for VNLR and 0.891 (95% CI: 0.861-0.922) for VNPR in the validation cohort. According to our results, models are useful in predicting in-hospital mortality risk due to COVID-19. The RIM Score proposed is a simple, widely available tool that can help identify patients at risk of fatal outcomes.