Bleeding Patterns and Prognostic Implications in Large-Core Ischemic Stroke: Insights from the TENSION Trial

BACKGROUND AND PURPOSE: In patients with large-core infarcts, the risk and clinical implications of posttreatment intracranial hemorrhage (ICH) remain poorly understood. We aimed to characterize the frequency, patterns, predictors, and prognostic relevance of posttreatment ICH in patients with large-core infarcts treated in the Efficacy and Safety of Thrombectomy in Stroke With Extended Lesion and Extended Time Window (TENSION) trial. MATERIALS AND METHODS: We performed a post hoc analysis of 253 patients with anterior circulation stroke and an ASPECTS score of 3-5 randomized to either mechanical thrombectomy (MT) plus best medical treatment (BMT) or BMT alone. Hemorrhages were categorized both clinically (symptomatic versus asymptomatic) and radiologically using the Heidelberg Bleeding Classification. Predictors of parenchymal hematoma (PH) and symptomatic intracranial hemorrhage (sICH) were identified using logistic regression. The association between bleeding severity and 90-day outcome was evaluated using multivariable models. RESULTS: = .004). Predictors of PH included MT itself (adjusted OR [aOR] 2.11; 95% CI, 1.11-3.99), higher NIHSS (aOR 1.13; 95% CI, 1.04-1.23), and larger core volume (aOR 1.003; 95% CI, 1.000-1.005). No independent predictors of sICH were identified. In adjusted models, bleeding severity was not associated with poor outcome, whereas age, NIHSS, and core volume were. Importantly, MT remained independently associated with better functional outcomes, even when adjusting for hemorrhagic events. However, the benefit of MT appeared attenuated in patients who developed PH, as shown by a significant treatment interaction. CONCLUSIONS: ICH is common in large-core stroke, particularly after MT, but is often asymptomatic and not independently linked to poor outcome. PH may reduce the benefit of MT, but overall, MT remains associated with improved functional outcomes. Distinguishing hemorrhage types is crucial in assessing posttreatment risk in this vulnerable population.