J Allergy Clin Immunol. 2020 Jul 22:S0091-6749(20)31027-7. doi: 10.1016/j.jaci.2020.07.009. Online ahead of print.
BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Since the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed.
OBJECTIVE: To develop a model able to predict the risk of fatal outcome in COVID-19 patients, which could be used easily upon arrival of patients to the hospital.
METHODS: We constructed a prospective cohort with 611 adult patients diagnosed with COVID-19 between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. We included in the analysis 501 patients who had been discharged or had died by April 20, 2020. The capacity to predict mortality of several biomarkers, measured at the beginning of hospitalisation, was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model.
RESULTS: High interleukin-6 (IL-6), C-reactive protein, lactate dehydrogenase (LDH), ferritin, D-dimer, neutrophil count, neutrophil-to-lymphocyte (N/L) ratio, and low albumin, lymphocyte count, monocyte count and peripheral blood oxygen saturation/fraction of inspired oxygen ratio (SpO2/FiO2), were all predictive of mortality (area under the curve (AUC)>0.70). A multivariable mortality risk model including SpO2/FiO2, N/L ratio, LDH, IL-6, and age, was developed and showed high accuracy for the prediction of fatal outcome (AUC=0.94). The optimal cut-off reliably classified patients into survivor and non-survivor, including patients with no initial respiratory distress, with 0.88 sensitivity and 0.89 specificity.
CONCLUSION: This mortality risk model allows early risk stratification of COVID-19 hospitalised patients, before the appearance of obvious signs of clinical deterioration, and can be used as a tool to guide clinical decision-making.