Development and validation of a prediction model for 30-day mortality in hospitalised patients with COVID-19: the COVID-19 SEIMC score

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

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 single-centre 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 (CKD-EPI) 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.