Background: Corona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. Methods: We retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. Results: A novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure ([≤]75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score [≥] 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of [≥] 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusion: Using this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.
A Novel Scoring System for Early Assessment of the Risk of the COVID-19-associated Mortality in Hospitalized Patients: COVID-19 BURDEN