The Predictive Value of the Verbal Glasgow Coma Scale in Traumatic Brain Injury: A Systematic Review.

Background: Traumatic brain injury (TBI) is a major global health concern, imposing significant burdens on individuals and healthcare systems. The Glasgow Coma Scale (GCS), a widely utilized instrument for evaluating neurological status, includes 3 variables: motor, verbal, and eye opening. The GCS plays a crucial role in TBI severity stratification. While extensive research has explored the predictive capabilities of the overall GCS score and its motor component, the Verbal Glasgow Coma Scale (V-GCS) has garnered less attention.

Objective: To examine the predictive accuracy of the V-GCS in assessing outcomes in patients with TBI, with a particular focus on functional outcome and mortality. In addition, we intend to compare its predictive performance with other components of the GCS.

Methods: A systematic review, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was conducted utilizing the PubMed, Scopus, and Web of Science databases. Inclusion criteria encompassed 10 clinical studies involving patients with TBI, wherein the level of consciousness was assessed using the verbal GCS score. Predominant statistical measures employed were odds ratios (ORs) and area under the curve (AUC).

Results: Recorded findings consistently underscore that lower V-GCS scores are associated with adverse functional outcomes and mortality in patients with TBI. Despite the predictive accuracy of the V-GCS, the Motor Glasgow Coma Scale (M-GCS) emerges as a superior predictor.

Conclusion: In the context of TBI outcome prediction, the V-GCS demonstrates its efficacy as a prognostic tool. However, the M-GCS exhibits superior performance compared with the V-GCS. These insights underscore the multifaceted nature of TBI assessment and emphasize the necessity of considering distinct components of the Glasgow Coma Scale for comprehensive evaluation. Further research is warranted to refine and improve the application of these predictive measures in clinical practice.