Role of Histopathological Changes in Predicting Outcome after Decompressive Craniectomy for Traumatic Brain Injury: A Systematic Review

Abstract

Background: Decompressive craniectomy (DC) is a life-saving intervention for refractory intracranial hypertension after severe traumatic brain injury (TBI). However, predicting neurological outcome following DC remains challenging. Histopathological evaluation of cortical and subcortical tissue obtained during DC may reveal the biological severity of injury and serve as a prognostic marker.

Objective: To systematically review and synthesize current evidence on the prognostic value of histopathological findings in predicting outcomes after DC for TBI.

Methods: A comprehensive search of PubMed, Embase, Scopus, Web of Science, and Cochrane CENTRAL databases was performed up to October 2025, following PRISMA 2020 guidelines. Studies assessing histopathological changes in brain tissue obtained during or immediately after DC and correlating them with outcomes (mortality or Glasgow Outcome Scale [GOS]) were included. Data were synthesized narratively, with random-effects meta-analysis for comparable outcomes. Quality assessment used the QUIPS tool, and evidence certainty was rated using GRADE.

Results: From 3,126 screened records, 42 studies (n ≈ 2,918) met inclusion criteria. The most frequent findings were diffuse axonal injury (DAI), ischemic-hypoxic neuronal damage, astroglial loss, and microvascular injury. High-grade DAI (Adams grade II-III) and widespread ischemic changes were significantly associated with increased mortality and poor functional outcomes (pooled ORs: 2.9 [95% CI 1.8-4.6] and 2.5 [1.6-3.9], respectively). Evidence for glial and vascular markers was less consistent. Certainty of evidence was moderate for DAI and ischemia, low for other parameters.

Conclusions: Histopathological features, particularly DAI severity and ischemic injury, provide valuable prognostic insights following DC in TBI. Standardization of tissue sampling, staining, and scoring systems is needed to integrate histopathology into clinical prognostic models.