Can serum biomarkers prognosticate out come of traumatic brain injury (TBI)?
Journal Title: Journal of Medical and Scientific Research - Year 2019, Vol 7, Issue 2
Abstract
Background: Studies regarding ability of serum markers to provide an early insight related to functional outcome of patients with traumatic brain injury (TBI) are limited. Objective: The objective of this study was to evaluate Glial Fibrillary Acidic Protein (GFAP) and S100B protein concentration in serum to predict outcome after traumatic brain injury. Methods and materials: Serum from 49 patients with traumatic brain injury (admission Glasgow Coma Scale ≤12) admitted in tertiary hospital for a period of one year was enrolled in study. All patients underwent an admission computed tomography (CT) scan as a part of their clinical evaluation. Serum levels of S100B & GFAP was measured using enzyme linked immunosorbent assay techniques. Demographic and outcome (Glasgow Outcome Scales) at 6th month of injury data were collected prospectively. Results: Serum levels of S100B and GFAP were elevated following TBI. Injury severity score showed significant positive Pearson’s correlation with S100B, but not with GFAP levels. GOS score at 6th month had significant negative correlation with S100B & GFAP levels. Median serum levels of GFAP & S100B were found to have increased in those patients who were dead compared to those alive, and likewise in patients with an unfavourable outcome compared to those with a favourable outcome. The Mann-Whitney test showed the statistical significance of the above results. Univariate logistic regression analysis revealed that both GFAP & S100B could help predict the outcome, along with GCS & CT scan brain. Multivariable analysis showed that S100B was the strongest in predicting outcome at 6 months of TBI (P<0.05). Conclusion: Serum levels of S100B and GFAP indicate the severity of brain damage and are correlated with neurological prognosis after TBI and may enhance prognostication when combined with clinical and radiological variables.
Authors and Affiliations
Raghavendra H, Varsha KS, Sobti S, Bhaskar S, Choudary A, Gupta LN
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