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

Abstract

ObjectiveIndividualized 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.MethodsMulticenter, 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.ResultsA 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.ConclusionConsidering 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.

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

“…Regarding the testing sample, two studies used hold-out test sets, respectively containing 208 patients ( 30 ) and 100 patients ( 35 ). The remaining studies performed cross-validation ( 23 – 26 , 28 , 29 , 31 – 34 , 36 , 37 ) or bootstrap approach ( 22 , 27 ). The five studies ( 23 , 29 , 31 , 34 , 35 ) used data obtained from MR CLEAN Registry ( 38 ).…”

“…Details of model development in 12 studies using conventional ML algorithms are shown in Table 1 . Tree models ( 22 , 24 , 31 ), random forests ( 23 , 26 , 27 ), and support vector machines ( 28 , 30 , 33 ) were each proposed by three studies, regularized logistic regression by two studies ( 25 , 32 ), and artificial neural networks by one study ( 29 ). To accommodate missing values, two studies used multiple imputation ( 23 , 29 ) and one used singular imputation ( 31 ), while other studies excluded participants with missing data in either predictive or outcome variables (complete-case analysis) ( 22 , 24 – 28 , 30 , 32 , 33 ).…”

“…The National Institutes of Health Stroke Scale and age were commonly ranked as the important predictors. All studies conducted internal validation, either by bootstrapping ( 22 , 27 ), hold-out validation ( 30 ), or k-fold cross-validation ( 23 – 26 , 28 , 29 , 31 – 33 ).…”

“…The basic characteristics of the eligible studies (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37) are summarized in Supplementary Table S4. The mean or median ages of the study participants ranged from 64.0 to 86.0 years, and the proportion of male participants ranged from 35.0 to 65.9%.…”

“…The five studies (23,29,31,34,35) used data obtained from MR CLEAN Registry (38). Fifteen studies reported the occlusion sites, of which 14 studies (22,23,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36) included patients with anterior…”