Predictors of disease severity and death in patients admitted with COVID-19 in infectious diseases center, Qazvin University of Medical Sciences, Iran: A cross-sectional study

Abstract

Background: The aim of this study was to determine the predictors of disease severity and death in patients hospitalized with coronavirus disease 2019 (COVID-19) in the Infectious Diseases Center of Qazvin University of Medical Sciences, Qazvin, Iran. Methods: A total of 228 patients with COVID-19 were included in this cross-sectional study. Of these, 114 patients were alive and 114 patients were dead, who were selected using the available sampling method. The logistic regression analysis method (Inter model) was used to predict the effective factors of severity of disease and mortality of patients and control of confounders. Findings: Multivariate logistic regression analysis showed that increase in C-reactive protein (CRP), blood urea nitrogen (BUN), and potassium (K), and decrease in the mean time from onset of symptoms to hospitalization could be predictors of death in patients with COVID-19. Moreover, the deceased patients were older than the surviving group. There was also a significant difference between the two groups of deceased and survivors in terms of the presence of underlying diseases of hypothyroidism and chronic kidney insufficiency (P < 0.001). Conclusion: According to the results of this study, measuring electrolytes at the beginning of hospitalization, and then serially, is recommended to take corrective measures in the field of disease control. © 2021 Isfahan University of Medical Sciences(IUMS). All rights reserved.