Biochemical Parameters Associated With COVID-19 Severity: A Statistical Analysis

Abstract

Objectives Coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2, is a contagious infectious disease. The aim of the present study is to identify the most effective biochemical parameters associated with COVID-19 severity. Early identification of these parameters is crucial for rapid risk stratification of patients and for initiating timely clinical management. Material and Methods A total of 584 patients with COVID-19 admitted to a tertiary care hospital in Mysuru, Karnataka, were included in the study. The routinely measured biochemical parameters obtained after hospital admission were evaluated for their association with the study outcome. Descriptive statistics, independent sample t -test, Pearson’s correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analysis were performed at a 5% level of significance using IBM SPSS Statistics version 22. Results Demographic and clinical characteristics showed that male patients, 427 (73%) and older adults aged 60–80 years, 157 (48.5%), constituted the largest proportion of hospitalised patients with COVID-19. A higher proportion of male patients was observed in both the ICU 16 (2.74%) and non-ICU 411 (70.38%) groups. Correlation analysis demonstrated mild to moderate associations among selected biochemical markers. In the multivariable logistic regression analysis, serum creatinine was the only biochemical parameter significantly associated with the study outcome (OR = 0.265; p = 0.027) and also demonstrated acceptable discriminatory performance in the ROC analysis. Conclusion Early identification of biochemical abnormalities improves clinical prognosis by enabling timely risk stratification and appropriate therapeutic intervention. These findings support the routine assessment of biochemical parameters as part of the initial evaluation of patients with COVID-19.