RESUMO
BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed. OBJECTIVE: Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital. METHODS: We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model. RESULTS: High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, d-dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO2/FiO2). A multivariable mortality risk model including the SpO2/FiO2 ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity. CONCLUSION: This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making.