Severe Brain Atrophy Predicts Poor Clinical Outcome After Endovascular Treatment of Acute Basilar Artery Occlusion: An Automated Volumetric Analysis of a Nationwide Registry

Background: Brain atrophy globally reflects the effects of preexisting risk factors and biological aging on brain structures and normally predicts poor outcomes in anterior circulation stroke. However, comparing with these patients, acute basilar artery occlusion (ABAO) impairs infratentorial regions frequently and might benefit from brain atrophy due to the resulting residual space to reduce tissue compression and thus improve prognosis, which raises doubts that current understandings for prognostic roles of brain atrophy are also applicable for ABAO. Therefore, this study aims to evaluate brain atrophy automatically from CT images and investigates its impact on outcomes of ABAO following endovascular treatment (EVT). Methods: A total of 231 ABAO who underwent EVT from the BASILAR registry were enrolled. Brain atrophy was quantified as the ratio of brain parenchymal volume to cerebrospinal fluid volume on baseline CT. The primary outcome was the modified Rankin Scale (mRS) score at 3 months. Results: The frequency of favorable outcomes (90-day mRS ≤ 3) was significantly lower in the severe atrophy group (P = 0.014). Adjusted logistic models revealed that severe brain atrophy was significantly negatively associated with favorable outcome incidence (P = 0.006), with no relationship with either in-hospital or 90-day overall mortality (all P > 0.05). Adding a severe atrophy index into the baseline model obviously enhanced its discriminatory ability in predicting the outcome by obviously increasing areas under the receiver operating characteristic curve, net reclassification improvement algorithm, and integrated discrimination improvement algorithm values (all P < 0.05). Conclusion: Severe brain atrophy did not improve in-hospital or overall mortality but impaired the long-term recovery after EVT. This objective and automated marker has the potential to be incorporated into decision-support methods for treating ABAO.