Comparative Analysis of Blood Biochemical Markers in COVID-19 vs. Non-COVID-19 Patients

Thesis Abstract

The study addresses the ongoing diagnostic and prognostic challenges posed by SARS-CoV-2 infection by evaluating whether routine blood biochemical markers differ systematically between COVID-19 positive and negative patients in a hospital setting. The aim is to delineate a biomarker signature that distinguishes COVID-19 cases from non-COVID-19 cases and to assess the relationship between biochemical profiles and clinical severity. Specific objectives include (1) comparing standard biochemical panels (lactate dehydrogenase, C-reactive protein, ferritin, D-dimer, transaminases, bilirubin, albumin, electrolytes, and renal function markers) between COVID-19 positive patients and matched non-COVID controls; (2) evaluating the association between biomarker abnormalities and disease severity indicators (need for oxygen therapy, intensive care admission, length of stay); (3) identifying independent biomarkers capable of predicting progression using multivariate regression; and (4) assessing the diagnostic performance of a biomarker panel through receiver operating characteristic (ROC) analysis. Theoretical framing integrates the systems biology perspective on host response to viral infection with the predictive utility framework of clinical biomarker research, anchoring hypotheses in the theory of inflammatory cascades and coagulation disturbances characteristic of viral pathophysiology. A multicenter, cross-sectional study will be conducted in three tertiary hospitals over 12 months. The population comprises adult inpatients presenting with symptoms suggestive of COVID-19 and tested with RT-PCR; a matched cohort of COVID-19 negative patients with similar exposure risk and presenting symptoms will serve as controls. A sample size of 600 participants (300 COVID-19 positive and 300 COVID-19 negative) provides >80% power to detect a medium effect size (Cohen’s d ? 0.5) in key biochemical markers at ? = 0.05. Consecutive sampling will be employed for eligible inpatients, with frequency matching on age (±5 years) and sex to minimize confounding. Data collection will utilize standardized electronic case report forms to capture demographic and clinical data, alongside laboratory data extracted from hospital information systems. The instruments include routine biochemistry analyzers calibrated per manufacturer protocols for markers such as LDH, CRP, ferritin, D-dimer, ALT/AST, total bilirubin, albumin, creatinine, BUN, electrolytes (Na, K, Cl), and glucose, complemented by clinical severity indices (SOFA, NEWS2) and outcome measures (ICU admission, mechanical ventilation, length of stay, mortality). Validity and reliability will be ensured through double data entry, cross-validation with laboratory reports, and calibration logs. Data analysis will follow a robust statistical plan. Descriptive statistics will summarize biomarker distributions; t-tests or Mann-Whitney U tests will compare COVID-19 positive and negative groups depending on data normality. Multivariate logistic regression will identify independent biomarkers associated with COVID-19 status, controlling for age, sex, body mass index, and comorbidities. Linear regression and Cox proportional hazards models will examine associations between biomarker levels and severity outcomes and time-to-event data, respectively. ROC curve analysis will quantify the diagnostic performance of individual markers and a composite biomarker panel, with area under the curve (AUC), sensitivity, specificity, and optimal cutoffs reported. Subgroup analyses will explore gender and age interactions, while sensitivity analyses will exclude patients with pre-existing inflammatory or hepatic conditions. All statistical tests will be two-tailed with significance set at p < 0.05. Missing data will be addressed using multiple imputation under the missing-at-random assumption. Expected findings include significantly higher inflammatory and coagulation markers (CRP, ferritin, D-dimer) and transaminases among COVID-19 patients, with a distinct pattern of hypoalbuminemia and renal function perturbations correlating with disease severity. It is anticipated that a panel incorporating CRP, ferritin, D-dimer, and albumin will demonstrate superior discriminatory power (AUC > 0.85) compared with single markers, and that biomarker abnormalities will independently predict ICU admission and longer hospital stays after adjustment for confounders. The study contributes to knowledge by refining understanding of the biochemical fingerprint of COVID-19 relative to non-COVID illnesses in the inpatient setting, informing risk stratification and resource allocation. It offers empirical evidence for a practical biomarker panel to support early diagnostic triage and prognosis, aligned with the inflammatory and coagulation cascade framework of viral infection. Anticipated recommendations include adoption of the biomarker panel into routine admission testing for patients with respiratory symptoms to enhance early detection, triage decisions, and targeted supportive care, with suggestions for longitudinal validation in diverse populations and exploration of mechanistic links between observed biochemical perturbations and organ-specific manifestations.