Persian risk scoring system for predicting hospital- based mortality between covid-۱۹ patients

Background: Coronaviruses (COVID-۱۹) are highly contagious viruses which cause symptoms ranging from the common flu to severe respiratory symptoms that cause critical illnesses and death. This study aimed to develop a predictive mortality scoring system for inpatients with COVID-۱۹ based on Demographic, clinical, and laboratory characteristics. Methods: This study accessed data from open-cohort of Isfahan, Khorshid COVID Cohort (KCC) study.۱۳۴ death cases were reported and we randomly selected ۲۷۰ cases from discharged patients. Clinical, and laboratory characteristics were used in logistic regression modeling to detect significant risk factors and develop the scoring system. ROC curve and AUC criteria was performed to evaluate the efficiency of the risk score developed at identifying patients with high-risk COVID-۱۹-related mortality Results: Six variables in the final logistic regression model were associated with outcome (discharged & death) in this cohort. These variables included Age (> ۶۵ vs ≤ ۵۵ years), SatO۲ (<۹۰ vs ≥ ۹۰), Comorbidities (yes vs no), WBC (<۴۵۰ or >۱۱۰۰۰ vs ۴۵۰-۱۱۰۰۰), AST (<۵ OR >۴۰ vs ۵-۴۰), and BUN (<۶ or >۲۴ vs ۶-۲۴). Using ROC curve analysis (specificity ۸۶% and specificity ۶۴%) and Yuden criterion (۰.۴۸۴), create a screening score (>۷) for identification of COVID-۱۹ cases at high risk of mortality. Conclusions: Using this scoring system in COVID-۱۹ patients, quickly and easily predicting COVID-۱۹ mortality can be identified.