Assessment of Inflammatory Biomarkers as Predictors of Disease Severity in Patients with Acute Respiratory Infections
Keywords:
Inflammatory biomarkers, Acute respiratory infections, IL-6, Procalcitonin, C-reactive protein, Disease severity, Predictive modelingAbstract
Purpose: This study investigates the prognostic value of key inflammatory biomarkers in determining disease severity among patients diagnosed with acute respiratory infections (ARIs). By evaluating a panel of routinely measured biomarkers, we aim to identify early indicators of clinical deterioration and improve risk stratification at admission.
Methodology: A prospective cohort design was employed in a tertiary care setting. Clinical data and blood samples from 312 patients presenting with ARIs were collected upon hospital admission. The biomarkers examined included C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT), ferritin, and neutrophil-to-lymphocyte ratio (NLR). Statistical associations between biomarker levels and disease outcomes—such as ICU admission, oxygen requirement, and 28-day mortality—were analyzed using multivariable logistic regression and receiver operating characteristic (ROC) curves.
Findings: Elevated levels of IL-6, PCT, and CRP showed strong correlations with clinical severity, particularly in predicting ICU admission and oxygen requirement. IL-6 demonstrated the highest predictive accuracy (AUC: 0.87), followed by PCT and CRP. Ferritin and NLR were moderately predictive. These biomarkers may thus serve as early warning indicators, supporting more timely and targeted interventions.
Practical implications: Incorporating these biomarkers into standard clinical triage algorithms could enhance early detection of high-risk ARI patients. This would enable clinicians to allocate critical care resources more effectively, particularly in resource-constrained environments.
Originality: This study extends knowledge by simultaneously evaluating multiple biomarkers in a real-world clinical population using robust multivariate modeling. The integrated analysis underscores the clinical utility of combining inflammatory markers for better predictive performance.
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Copyright (c) -1 Oluwatobi Ifeoma Lawal (Author)
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