Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score - Cervero, Miguel; Berenguer, Juan; Borobia, Alberto M; Ryan, Pablo; Rodríguez-Baño, Jesús; Bellón, Jose M; Jarrín, Inmaculada; Carratalà, Jordi; Pachón, Jerónimo; Carcas, Antonio J; Yllescas, María; Arribas, José R (BMJ Group, 2021)
Objective To develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms. Design Multivariable prognostic prediction model. Setting 127 Spanish hospitals. Participants Derivation (DC) and external validation (VC) cohorts were obtained from multicentre and singlecentre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively. Interventions Prognostic variables were identified using multivariable logistic regression. Main outcome measures 30-day mortality. Results Patients¿ characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806¿0.837) in the DC and 0.845 (0.819¿0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0¿2 points (0%¿2.1%), moderate with 3¿5 (4.7%¿6.3%), high with 6¿8 (10.6%¿19.5%) and very high with 9¿30 (27.7%¿100%). Conclusions A simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19.