A Scoring System To Predict Mortality In Patients Admitted To Hospital With Covid-19

Abstract Background: Mortality from COVID-19 has reached rates approaching 13,0%, and it is necessary to have tools to predict the course of the disease, risk of aggravation and probability of death. We propose a predictive mortality score in patients admitted with COVID-19. Methods: We have collected and analysed more than 50 epidemiological, clinical, analytical and treatment variables in a referral cohort of 303 patients admitted for COVID-19. Those variables retained after multivariate analysis that compared survivors and non-survivors patients became the components of the risk of death score. To check the validity of the score, a validation cohort of patients admitted for COVID-19 was used. Results: Mortality was 17% in the referral cohort. Candidate variables to predict risk of death were age ≥65 years, cardiovascular disease, dyspnoea, pneumonia, acute respiratory distress, non-invasive mechanical ventilation, abnormal prothrombin, elevated D-dimer, and abnormal lactate dehydrogenase. The proposed cut-off point in the scale was 7 (with 0-6 points representing a low risk of death and 7-17 a high risk). Application of the score in the validation cohort obtained a sensitivity of 100% and a specificity of 92%, with a positive predictive value of 71% and a negative predictive value of 100%. Conclusions : Our study presents for the first time the development and validation of a risk-of-death scoring system for patients hospitalised with COVID-19 using clinical and laboratory parameters that can be retrieved from patients’ admission records.