Citations

Intracerebral haemorrhage (ICH) and small vessel ischaemic stroke (SVS) are the most severe manifestations of cerebral small vessel disease. In a cross-phenotype genome-wide association analysis, Chung et al. identify two novel associations at 2q33 and 13q34 plus a previously identified locus at 1q22 for non-lobar ICH and SVS risk.

Background and Purpose— We evaluated deep learning algorithms’ segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods— Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms’ performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results— The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P <0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm 3 (0.9–16.6 cm 3 ). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P <0.0001) and different topography compared with other stroke subtypes. Conclusions— Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.

Introduction: Cerebral small-vessel disease (SVD) is associated with increased stroke risk and poor stroke outcomes. We aimed to evaluate if chronic SVD burden is associated with poor recruitment of collaterals in large-vessel occlusive stroke. Methods: Consecutive patients with MCA±ICA occlusion presenting within 6 hours after stroke symptoms onset from 2012- 2016 were included. Primary outcome was collateral flow, which was assessed on baseline CT-angiogram (poor ≤50% filling, good >50% filling). Markers of chronic SVD on brain MRI were rated for the presence of WMH (Fazekas scale 2-3), enlarged perivascular spaces (EPVS), chronic lacunae, and any cerebral microbleeds (CMB) using the STRIVE criteria. Severity of SVD was quantified by adding the presence of each SVD feature to a maximum score of 4. Multivariable logistic regression models were performed to evaluate the relationships between SVD and poor collaterals. Results: Of the 100 patients, the mean age was 65±16, median NIHSS was 15, and 68% had SVD. Poor collaterals were observed in 46%, and those with SVD were more likely to have poor collaterals (aOR 1.9; 95%CI 1.1-3.2) than patients without SVD. Of the SVD types, poor collaterals were associated with WMH (aOR 2.9 per Fazekas increment; 95%CI 1.6-5.3) but not EPVS (aOR 1.3; 0.4-4.0), lacunae (aOR 2.1; 0.6-7.1), or CMB (aOR 2.1; 0.6-7.8). Having a greater number of different SVD markers was associated with a higher odds of poor collaterals (crude trend p<.001; adjusted p=.056). There was a dose-dependent relationship between WMH burden and poor collaterals: adjusted odds of poor collaterals were 1.5, 3.0, and 9.7 across Fazekas scores of 1-3 (trends P=.015, Table). No patient with SVD score of 4 had good collaterals. Conclusions: Chronic cerebral small-vessel disease is associated with poor recruitment of collaterals in large-vessel occlusive stroke. A prospective study to elucidate the mechanism of how SVD may impair the recruitment of collaterals is ongoing.

Introduction: Robust leptomeningeal collaterals protect brain from ischemia, yet the determinants of poor collaterals remains unclear. We aimed to identify predictors of poor collaterals in patients with large-vessel occlusive stroke. Methods: We analyzed 112 consecutive patients with middle cerebral artery ± internal carotid artery occlusion from 2012-2016 who presented within 6 hours after stroke symptoms onset. Collaterals on baseline CT-angiogram (poor collaterals ≤50% filling, good collaterals >50% filling) were reviewed blinded to clinical data. Pre-specified predictors of poor collaterals were analyzed with adjusted logistic regression models: including known predictors (age, vascular risk factors and metabolic derangements) and hypothesized predictors (low cardiac output, elevated inflammatory markers, and imaging evidence for chronic cerebral small-vessel disease and chronic steno-occlusive arterial disease). Results: The mean age was 67± 16 years, there were 48% women, 57% white, and median NIHSS was 15 (IQR 11-20). Poor collaterals at baseline were detected in 48%. Multivariable modeling identified that age (aOR 1.8 per 10 years; 95% CI 1.3-2.6; p<0.001), black race (aOR 2.1; 95% CI 1.0-4.5; p=0.043), high blood glucose level (aOR 2.4 per 100 mg/dl; 95% CI 1.1-5.1; p=0.029), and high WMH burden (aOR 6.1; 95%CI 1.8-20.4; p=0.003) were independent predictors of poor collaterals. Associations between low cardiac output, elevated inflammatory markers, intracranial atherosclerosis and poor collaterals did not reach statistical significance (Table). Conclusions: We have identified that advanced age, black race, hyperglycemia, and white matter hyperintensities are predictors of poor collaterals. Independent replication study at another institution is ongoing to further explore the hypothesized predictors.

Background: The importance of physical activity in preventing major vascular events has received increased attention in the past decade. Due to the inclusion of the INTERVENT lifestyle modification program, the CREST2 trial provides a unique opportunity to study physical activity profiles in patients with severe asymptomatic extracranial carotid stenosis. Hypothesis: Based on data from a trial of intracranial stenosis patients, we aimed to evaluate the hypothesis that <40% of carotid stenosis subjects will have optimal physical activity levels. We also assessed key subgroups to determine patterns of physical activity. Methods: We analyzed 1087 CREST2 participants with baseline data on physical activity. The Physician-based Assessment and Counseling for Exercise (PACE) score is recorded for each patient and dichotomized as “in target” (4-8 points) or “out of target” (<4 points). A PACE score of <4 indicates a subject who does not engage in regular exercise. Results are analyzed for the entire trial. Chi square testing and t-tests were performed for evaluation of subgroup differences. Results: The mean age of the study population is 69.5±7.8 years (60% male). The median PACE score at baseline is 4.0 (mean 3.8±2.1; IQR 2 to 6). The Table shows baseline characteristics of patients by mean PACE score. Being male, not having diabetes mellitus, having a body mass index <30kg/m2, being a non-smoker, and not having a history of peripheral arterial disease were associated with significantly higher mean PACE scores (P<0.01). Conclusions: We have defined several patient groups with asymptomatic carotid stenosis that have suboptimal physical activity. These results provide opportunities for targeted efforts to improve primary stroke prevention. These baseline data will also allow investigators to determine if trial involvement and the INTERVENT program leads to improvement in the level of physical activity.

Introduction: Across 269 international sites, the POINT trial randomly assigned 4881 patients with minor ischemic stroke or high-risk transient ischemic attack to receive either clopidogrel at a loading dose of 600 mg on day 1 followed by 75 mg per day, plus aspirin at a dose of 50-325 mg per day or the same range of doses of aspirin alone. Dual antiplatelet therapy significantly reduced the incidence of major ischemic events, but also significantly increased the incidence of major hemorrhage. Clopidogrel is a prodrug that requires metabolism by the cytochrome P450 complex to act on the P2Y12 platelet receptor. CYP2C19 (cytochrome P450 family 2 subfamily C polypeptide 19) variants are associated with variable rates of metabolism of clopidogrel and thus pharmacogenomic effects may be clinically relevant. Methods: Genomic DNA was extracted from peripheral blood monocytes using the standard protocols. Genotyping for CYP2C19 *2, *3 and *17 alleles (rs4244285, rs4986893 and rs12248560) defines metabolizer/activator status and was performed using custom TaqMan Allelic Discrimination Assays on a QuantStudio™ 7 Flex Real-Time PCR System. Results: As of June 28, 2018, a total of 445 trial participants had DNA available for analysis. CYP2C19 allele genotyping demonstrated approximately one quarter of subjects (n=127; 28.6%) would be defined as poor or Intermediate metabolizers of clopidogrel. Conclusions: Based on targeted cytochrome P 450 genotyping, about one quarter of POINT patients have reduced clopidogrel activation. Additional genotyping is ongoing. We plan to present results assessing whether the treatment effect of clopidogrel is limited to those with CYP2C19 genotypes associated with normal drug metabolism.