RESUMO
OBJECTIVE: Individualized patient selection for mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large ischemic core (LIC) at baseline is an unmet need. We tested the hypothesis that assessing the functional relevance of both infarcted and hypoperfused brain tissue would improve the selection framework of patients with LIC for MT. METHODS: We performed a multicenter, retrospective study of adults with LIC (ischemic core volume >70 mL on MRI diffusion-weighted imaging) with MRI perfusion treated with MT or best medical management (BMM). Primary outcome was 3-month modified Rankin Scale (mRS), favorable if 0-3. Global and regional eloquence-based core perfusion mismatch ratios were derived. The predictive accuracy for clinical outcome of eloquent regions involvement was compared in multivariable and bootstrap random forest models. RESULTS: A total of 138 patients with baseline LIC were included (MT n = 96 or BMM n = 42; mean age ± SD, 72.4 ± 14.4 years; 34.1% female; mRS 0-3: 45.1%). Mean core and critically hypoperfused volume were 100.4 mL ± 36.3 mL and 157.6 ± 56.2 mL, respectively, and did not differ between groups. Models considering the functional relevance of the infarct location showed a better accuracy for the prediction of mRS 0-3 with a c statistic of 0.76 and 0.83 for logistic regression model and bootstrap random forest testing sets, respectively. In these models, the interaction between treatment effect of MT and the mismatch was significant (p = 0.04). In comparison, in the logistic regression model disregarding functional eloquence, the c statistic was 0.67 and the interaction between MT and the mismatch was insignificant. CONCLUSIONS: Considering functional eloquence of hypoperfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, in patients with AIS and LIC, considering the functional eloquence of the infarct location improves prediction of disability status at 3 months.