Abstract

This study aimed to identify laboratory predictors of mortality in individuals hospitalized with COVID-19, to establish a tool for identifying high-risk patients. The research included all patients admitted to the hospital due to COVID-19, totalling 201 individuals with a median age of 54.36 ± 13.51 years. Clinical outcomes revealed that 11.2% of patients were admitted to the Intensive Care Unit (ICU), 9.1% required mechanical ventilation support, and the overall mortality rate was <10%. Laboratory biomarkers deemed significant for mortality prediction were incorporated into a risk score calculation. Multivariate logistic regression model, considering parameters such as haemoglobin, erythrocytes, leukocytes, neutrophils, lymphocytes, creatinine, and other parameters along with demographic determinants have been used. A retrospective cohort study was conducted. Data were presented using frequency tables. Predictors of COVID-19 severity were identified using a multiple regression model, with adjusted relative risk (ARR), p-value, and 95% confidence interval (CI), to assess the significance. The presence of a statistically significant association between explanatory variables and COVID-19 outcomes was analyzed using a Binary Logistic regression model, with adjusted odds ratio (AOR), p-value, and 95% CI for AOR utilized for significance testing.

Description