Perfusion Imaging to select patients with large ischemic core for mechanical thrombectomy

International audienceBackground and purpose: Patients with acute ischemic stroke, proximal vessel occlusion and a large ischemic core at presentation are commonly not considered for mechanical thrombectomy (MT). We tested the hypothesis that in patients with baseline large infarct cores, identification of remaining penumbral tissue using perfusion imaging would translate to better outcomes after MT.Methods: This was a multicenter, retrospective, core lab adjudicated, cohort study of adult patients with proximal vessel occlusion, a large ischemic core volume (diffusion weighted imaging volume ≥70 mL), with pre-treatment magnetic resonance imaging perfusion, treated with MT (2015 to 2018) or medical care alone (controls; before 2015). Primary outcome measure was 3-month favorable outcome (defined as a modified Rankin Scale of 0-3). Core perfusion mismatch ratio (CPMR) was defined as the volume of critically hypo-perfused tissue (Tmax >6 seconds) divided by the core volume. Multivariable logistic regression models were used to determine factors that were independently associated with clinical outcomes. Outputs are displayed as adjusted odds ratio (aOR) and 95% confidence interval (CI).Results: A total of 172 patients were included (MT n=130; Control n=42; mean age 69.0±15.4 years; 36% females). Mean core-volume and CPMR were 102.3±36.7 and 1.8±0.7 mL, respectively. As hypothesized, receiving MT was associated with increased probability of favorable outcome and functional independence, as CPMR increased, a difference becoming statistically significant above a mismatch-ratio of 1.72. Similarly, receiving MT was also associated with favorable outcome in the subgroup of 74 patients with CPMR >1.7 (aOR, 8.12; 95% CI, 1.24 to 53.11; P=0.028). Overall (prior to stratification by CPMR) 73 (42.4%) patients had a favorable outcome at 3 months, with no difference amongst groups.Conclusion: s In patients currently deemed ineligible for MT due to large infarct ischemic cores at baseline, CPMR identifies a subgroup strongly benefiting from MT. Prospective studies are warranted

Relevance of Brain Regions' Eloquence Assessment in Patients With a Large Ischemic Core Treated With Mechanical Thrombectomy.

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 the infarcted and hypo-perfused brain tissue, would improve the selection framework of patients with LIC for MT. METHODS Multicenter, retrospective, study of adult with LIC (ischemic core volume > 70ml on MR-DWI), with MRI perfusion, treated with MT or best medical management (BMM).Primary outcome was 3-month modified-Rankin-Scale (mRS), favourable 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±14.4years; 34.1% females; mRS=0-3: 45.1%). Mean core and critically-hypo-perfused volume were 100.4ml±36.3ml and 157.6±56.2ml 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. CONCLUSION Considering functional eloquence of hypo-perfused tissue in patients with a large infarct core at baseline allows for a more precise estimation of treatment expected benefit