DOI 10.1007/s43678-026-01218-y
Brain injury guidelines for the management of traumatic brain injury: a systematic review and meta-analysis.
Аннотация
OBJECTIVES
The Brain Injury Guidelines (BIG) serve to predict the severity of traumatic brain injuries resulting in radiologic findings. The use of BIG promises significant time and resource savings, over routine neurosurgical consultation and repeat imaging. After more than a decade of validation, studies have demonstrated BIG to reliably predict adverse outcomes. We sought to analyze the existing literature to determine the pooled efficacy of BIG and its variations.
METHODS
A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines (PROSPERO ID: CRD42023481186). Seven databases were searched, and articles published from inception to October 2025 were included. Pooled sensitivities and specificities were calculated using random effects meta-analysis. The main outcomes of interest were neurologic deterioration, neurosurgical intervention and death. Bias was assessed using a modified PROBAST tool.
RESULTS
From the 4,733 studies screened, 47 articles were identified for inclusion, of which 23 studied the original BIG and were suitable for meta-analysis. The remaining 2 used BIG variants, from which, there was insufficient data to conduct meta-analyses. The pooled sensitivity and specificity for BIG1 to rule out neurological deterioration, neurosurgical intervention and death were 98.3% (95%CI 95.7-99.4) and 12.7% (95%CI (1.5-58.7)), 98.7% (95%CI 97.4-99.4) and 14.4%(95%CI 3.3-45.8), and 98.2% (95%CI 96.6-99.1) and 12.8% (95%CI 3.0- 41.0) respectively.
CONCLUSIONS
BIG is highly sensitive for predicting adverse outcomes among traumatic brain injury patients. BIG may be used to identify patients appropriate for discharge, without further immediate investigations or consultation for their traumatic brain injury. It may also play a role in reducing unnecessary transfers for neurosurgical consultation. BIG specificity is low, however, further study of BIG variants may offer solutions to the BIG's shortcomings.
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