Risk Factors and a Novel Score (CARI-65) Predicting Mortality in COVID-19 Patients

Purpose: The rapid spread of severe acute respiratory syndrome coronavirus-2 infection resulted in an exponential increase in hospitalizations and mortality. We aimed to explore the determinants of mortality and formulate a score that can predict mortality in patients hospitalized due to coronavirus disease 2019 (COVID-19). Materials and Methods: In this retrospective study, 1024 COVID-19 patients hospitalized between March 2020 and October 2020 were included. Patient demographics, underlying comorbid illnesses, clinical features, vital signs at admission, disease severity, and laboratory parameters, were collected from hospital medical records and analyzed to derive risk factors for in-hospital mortality and formulate a mortality prediction score. Results: The median age of the study population was 56 years (interquartile range [IQR], 45-65) and was significantly higher in nonsurvivors than in survivors (62 [IQR 55-70] vs. 52 [IQR 40-65]; P = 0.001). Hypertension and diabetes were the most common associated comorbid illnesses seen in 50.5% (n = 518) and 29.1% (n = 299) of patients, respectively. The presence of altered level of consciousness (C), azotemia with serum creatinine >1.5 mg/dl (A), respiratory rate >25/min (R), interleukin-6 >25 pg/ml (I), and age >= 65 years were independent predictors of mortality. A six-point COVID-19 mortality prediction score, "CARI-65," was developed using variables predicting mortality in multivariate regression analysis. The CARI-65 score >= 3 had a sensitivity and specificity of 87.1% and 57.3%, respectively, and positive and negative predictive values of 42.52% and 92.45%, respectively, in predicting mortality. Conclusion: This study demonstrated various demographic, clinical, and laboratory parameters that predict mortality in hospitalized COVID-19 patients. We also proposed a simple risk stratification score to predict mortality in hospitalized COVID-19 patients, so that effective triaging of patients can be done to utilize health-care resources efficiently.