Objective:

This network meta-analysis (NMA) evaluated the comparative performance of glial and neuronal biomarkers for mortality and unfavorable functional outcomes in adults with moderate to severe traumatic brain injury (TBI).

Background:

TBI is considered one of the leading causes of morbidity and mortality worldwide. Biomarkers like S100B, Glial Fibrillary Acidic Protein (GFAP), Neuron-Specific Enolase (NSE), and Ubiquitin Carboxy-terminal Hydrolase L1 (UCH-L1) have shown promising prospects in outcome prediction, though their performance varies across studies.

Design/Methods:

An NMA following the PRISMA guidelines was conducted on studies assessing multiple biomarkers to predict unfavorable outcomes following TBI, specifically mortality and poor functional outcomes (Glasgow Outcome Scale [GOS], GOS-Extended [GOSE]). A Bayesian framework with three Markov Chain Monte Carlo (MCMC) chains, a random-effects model, and Log Odds Ratios (LOR) transformed into Odds Ratios (OR) was used for comparisons, with GFAP as the reference. Heterogeneity was assessed via the I² statistic. Rank probabilities determined each biomarker's relative performance.

Results:

Seven studies comprising 522 patients were included. For mortality, GFAP vs. NSE (OR 1.003, 95% CI: 0.53-1.84), GFAP vs. S100B (OR 0.88, 95% CI: 0.50-1.55), and GFAP vs. UCH-L1 (OR 0.80, 95% CI: 0.45-1.46) showed minimal heterogeneity (I²=4%). NSE had the highest probability of being the most effective biomarker (39.96%), followed by GFAP (35.9%). For unfavorable functional outcomes, GFAP vs. NSE (OR 0.82, 95% CI: 0.28-2.14), GFAP vs. S100B (OR 1.1, 95% CI: 0.42-2.7), and GFAP vs. UCH-L1 (OR 1.1, 95% CI: 0.23-4.6) had low heterogeneity (I²=14%). UCH-L1 had the highest probability of being the most effective (40.2%), followed by S100B (36.2%).

Conclusions:

NSE was most effective for predicting mortality, while UCH-L1 ranked highest for predicting unfavorable functional outcomes. All biomarkers showed consistent findings with low heterogeneity across studies. Future research should focus on the clinical application of these biomarkers in decision-making settings.