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Background: Ischemic stroke (IS) is a leading cause of long-term disability with sex-specific differences in outcomes. Identifying the influential factors that contribute to sex-specific disparities in stroke outcomes, therefore, holds potential to develop individualized interventions for reducing long-term disability. Further, investigating the association between sex and Patient-Reported Outcome Measures (PROMs) provides additional information on the individual impact and heterogeneity of IS. We aimed to identify sex-specific differences in stroke outcomes and relationship with PROMs in IS patients with 3-month follow-up. Methods: Between February 2017 and February 2020, a total of 410 patients admitted with IS to the Massachusetts General Hospital, in Boston, were enrolled in this prospective cohort. At 3-month poststroke, patients were assessed for Barthel Index, modified Rankin Scale, and PROM-10 questionnaires. T scores for physical and mental health were determined from the summing of PROM-10 responses in each domain. Regression analysis was performed to identify sex-specific determinants of functional and patient-reported outcomes. Results: At baseline, 242 participants were male (mean age, 65 years) and 168 were female (mean age, 70 years). Groups had similar rates of cardiovascular risk factors, admission National Institutes of Health Stroke Scale, and discharge modified Rankin Scale. At follow-up, male participants were more likely to have better rates of T Physical and Barthel Index. In regression analysis, PROMs T Physical (odds ratio, 1.06; P =0.01), Barthel Index (odds ratio, 1.06; P =0.01), and modified Rankin Scale score of ≥2 (odds ratio, 2.60; P =0.01) were associated with female sex. Female sex was also associated with lower scores for PROMs Physical subcomponents and with patient-reported general health and emotional problems. Conclusions: Women have worse outcomes after ischemic stroke, including objective measures of functional disability and patient-reported outcomes. Incorporating PROMs into IS outcome measures may offer additional insight into sex-specific differences in stroke recovery and outcomes.

BackgroundAccess to endovascular thrombectomy (EVT) is relatively limited. Hub‐and‐spoke networks seek to transfer appropriate large‐vessel occlusion stroke candidates to EVT‐capable hubs. However, some patients are ineligible upon hub arrival, and factors that drive transfer inefficiencies are not well described. We sought to quantify EVT transfer efficiency and identify reasons for EVT ineligibility.MethodsConsecutive EVT candidates presenting to 25 spokes from 2018 to 2020 with pretransfer computed tomography angiography‐defined large‐vessel occlusion and Alberta Stroke Program Early Computed Tomography Score of ≥6 were identified from a prospectively maintained database. Outcomes of interest included hub EVT, reasons for EVT ineligibility, and 90‐day modified Rankin scale score of ≤2.ResultsAmong 258 patients, the median age was 70 years (interquartile range, 60–81 years); 50% were women. A total of 56% were ineligible for EVT after hub arrival. Cited reasons were large established infarct (49%), mild symptoms (33%), recanalization (6%), distal occlusion (5%), subocclusive lesion (3%), and goals of care (3%). Late window patients (last known well >6 hours) were more likely to be ineligible (67% versus 43%;PP=0.04), had lower National Institutes of Health Stroke Scale score (10 versus 16;PPPP=0.04) compared with eligible patients. EVT ineligibility independently reduced the odds of 90‐day modified Rankin scale score of ≤2 (adjusted odds ratio, 0.26; 95% CI, 0.12–0.56;P=0.001) when controlling for age, National Institutes of Health Stroke Scale score, and last known well‐to‐hub arrival time.ConclusionsAmong patients transferred for EVT, there are multiple reasons for ineligibility upon hub arrival, with most excluded for infarct growth and mild symptoms. Understanding factors that drive transfer inefficiencies is important to improve EVT access and outcomes.

Ischemic cerebrovascular events often lead to aphasia. Previous work provided hints that such strokes may affect women and men in distinct ways. Women tend to suffer strokes with more disabling language impairment, even if the lesion size is comparable to men. In 1,401 patients, we isolated data-led representations of anatomical lesion patterns and hand-tailored a Bayesian analytical solution to carefully model the degree of sex divergence in predicting language outcomes ∼3 months after stroke. We located lesion-outcome effects in the left-dominant language network that highlight the ventral pathway as a core lesion focus across different tests of language performance. We provide newly detailed evidence for sex-specific brain-behavior associations in the domain-general networks associated with cortico-subcortical pathways, with unique contributions of the fornix in women and cingular fiber bundles in men. Our collective findings suggest diverging white matter substrates in how stroke causes language deficits in women and men. Clinically acknowledging such sex disparities has the potential to improve personalized treatment for stroke patients worldwide.

This study aimed to investigate the influence of stroke lesions in pre-defined highly interconnected (rich club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance.We analyzed MRI data recorded at index stroke and ∼3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke (AIS) enrolled in the multisite MRI-GENIE study. Structural stroke lesions were spatially normalized and parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS>2) was modeled in a Bayesian logistic regression framework that relied on both lesion location, as well as the covariates: age, sex, total DWI lesion volume and comorbidities. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. Via model comparisons, we first tested whether the rich club region model was superior to a baseline model considering clinical covariates and lesion volume only. In spatial specificity analyses, we randomized the split into “rich club” and “non-rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1,000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club region effects.A total of 822 patients (age: 64.7 (standard deviation: 15.0), 39% women, 27.7% with mRS>2) were analyzed. The rich club model substantially outperformed the baseline model (weights of model comparison: rich club model: 0.96; baseline: 0.04). Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations (15/1,000 random constellations with higher mean posterior values). Among the these 15 random constellations with higher means, the most frequently selected regions were the inferior temporal gyrus (posterior division, 8/15), the putamen (8/15), the cingulate gyrus (7/15) and the superior parietal lobule (6/15). Rich club regions were substantially more important in explaining long-term outcome in women than in men (mean of the difference distribution:-0.107, 90%-HDPI:-0.193 to -0.0124).Lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific, i.e., the majority of random combinations of six regions had comparably smaller effects on long-term outcome. Effects were substantially more pronounced in women.

Background and ObjectivesThe care of patients with large vessel occlusion (LVO) stroke has been revolutionized by endovascular thrombectomy (EVT). While EVT has a large effect size, most patients treated with EVT remain disabled or die within 90 days. A better understanding of outcomes may influence EVT selection criteria, novel therapies, and prognostication. We sought to identify associations between outcomes and brain regions involved in ischemic lesions.MethodsFor this cohort study, consecutive patients with LVO who were treated with EVT and underwent post-EVT MRI were identified from a tertiary referral center (2011–2019). Acute ischemic lesions were manually segmented from diffusion-weighted imaging and spatially normalized. Individual lesions were parcellated (atlas-defined 94 cortical regions, 14 subcortical nuclei, 20 white matter tracts) and reduced to 10 essential lesion patterns with the use of unsupervised dimensionality reduction techniques. Ninety-day modified Rankin Scale (mRS) score (>2) was modeled via bayesian regression, taking the 10 lesion patterns as inputs and controlling for lesion size, age, sex, acute NIH Stroke Scale (NIHSS) score, alteplase, prior stroke, intracerebral hemorrhage, and good reperfusion (Thrombolysis in Cerebral Infarction 2b–3). In comparative analyses, 90-day mRS score was modeled considering covariates only, and compartment-wise relevances for acute stroke severity and 90-day mRS score were evaluated.ResultsThere were 151 patients with LVO identified (age 68 ± 15 years, 52% female). The median NIHSS score was 16 (interquartile range 13–20); 56% had mRS score >2. Lesion locations predictive of 90-day mRS score involved bilateral but left hemispherically more pronounced precentral and postcentral gyri, insular and opercular cortex, and left putamen and caudate (area under the curve 0.91, highest probability density interval [HPDI] covering 90% certainty 0.90–0.92). The lesion location model outperformed the simpler model relying on covariates only (bayesian model comparison of 97% weight to the model with vs 3% weight to the model without lesion location). While lesions affecting subcortical nuclei had the highest relevance for stroke severity (posterior distribution mean 0.75, 90% HPDI 0.256–1.31), lesions affecting white matter tracts had the highest relevance for 90-day mRS score (0.656, 90% HPDI 0.0864–1.12).DiscussionThese data describe the significance for outcomes of specific brain regions involved in ischemic lesions on MRI after EVT. Future work in additional datasets is needed to confirm these granular findings.

Introduction: Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. While coma has been studied in the context of traumatic brain injury, the epidemiology and pathophysiology of coma in ischemic stroke has been underexplored. We sought to characterize the incidence and clinical features of coma as a presentation of large vessel occlusion (LVO) stroke. Methods: Individuals who presented with LVO were identified from July 2018 to December 2020. Coma was defined as an unresponsive state of impaired arousal and awareness, operationalized as a score of 3 on NIHSS item 1a: responds only with reflex motor or autonomic effects or totally unresponsive, flaccid, and areflexic. Results: A total of 28/638 (4.4%) patients with large vessel occlusion stroke were identified as presenting with coma. The median age was 65 (IQR 48-78), and 57% were female. The median NIHSS was 32 (IQR 29-34). Occlusion locations included basilar (10), vertebral (2), P1 posterior cerebral (2), internal carotid (4), M1 middle cerebral (6), and M2 middle cerebral arteries (4). In all 14 patients with anterior LVO, the acute LVO was unilateral. Of these 14, 6 had evidence of acute or chronic stroke involving the contralateral hemisphere; 1 experienced seizure; 1 experienced cardiac arrest; and 1 had chronic occlusion of the contralateral ICA. Overall, 20/28 died during the admission. Of those who died, 9 presented with anterior LVO and 11 with posterior LVO. Eight patients were treated with EVT. For those not treated, reasons included established infarct with ASPECTS Conclusions: It is not uncommon for patients with LVO stroke to present with coma, and 65% of patients not treated with EVT had delayed presentations or large established infarcts, emphasizing the need for maintaining a high index of suspicion. While more commonly thought to result from posterior LVO, coma in our cohort was similarly likely to result from anterior LVO. Further study of these patients may shed light on the pathophysiology of coma. Efforts to improve early diagnosis and care are crucial given their poor outcomes.

Background: 24h CT is routinely acquired post-thrombolysis. Parenchymal hematoma and brain edema are feared complications. Predicting complications that change management would help reduce unnecessary imaging. Objective: To evaluate the utility of 24h per-protocol CT scan for stroke patients after thrombolysis. Methods: A prospectively collected cohort of consecutive patients presenting with acute ischemic stroke to our hospital from 2013- 2021 who were treated with IV tPA without undergoing endovascular therapy was entered into our IRB-approved stroke registry. Neuroimaging performed within 48h after admission was reviewed. Patients’ demographics, past medical history, admission National Institute of Health Stroke Scale (NIHSS) score, and medical variables were obtained. The primary outcome was significant radiologically findings (parenchymal hematoma or midline shift/herniation) leading to change in management (additional monitoring, emergent procedure, administration of mannitol or alteration in blood pressure goal). Results: From 2013 to 2021, 875 patients (mean age 69) who had IV tPA without undergoing endovascular therapy were identified, of which 472 (47.1%) were female. Median (IQR) NIHSS score was 9 (4-16) on admission. Imaging findings: 339 (38.7%) had expected evolving infarctions; 106(13.3%) developed any type of hemorrhagic transformation, including 66 (7.5%) who had hemorrhagic infarction and 50 (5.7%) parenchymal hematomas; 142(16.2%) developed any kind of mass effect, including 63 (7.2%) who had mild mass effect and 79 (9.0%) midline shift/herniation. There were 124 patients (12.1%) had significant findings (including parenchymal hematoma and midline shift/brain herniation) leading to change in the management. Patients who had significant findings had higher NIHSS score 16 (8-21) than those without 8 (4-15) (p Conclusions and interpretations: Our study showed that patients with high NIHSS score were more likely to have significant findings. 24-hour follow-up CT scan should be based on the risk of developing significant complications.

Introduction: Predicting individual outcomes post-stroke with the highest possible accuracy is a crucial steppingstone in the realization of precision medicine. We here evaluated various types of lesion information in their capacity to predict stroke severity in a large cohort of patients with acute ischemic stroke. Methods: A total of 1,075 patients of the MRI-GENIE study [age: 64.2(14.7), 38% women] contributed to analyses (N=792 as train and N=283 as test sample). We employed ridge regression with hyperparameter optimization and nested five-fold cross-validation to predict acute NIHSS-based stroke severity. Our baseline model considered DWI lesion volume. Further models tested structural lesion location, indirect structural and functional lesion connectivity, individually and combined with lesion volume. Data was preprocessed by principal component analysis (PCA), retaining 95% of the variance of the original data. Model performance was compared in terms of explained variance (R-squared) and 95% confidence intervals in the outer loop of the training set and the test set. Results: Structural lesion connectivity enabled the highest prediction performance in the test set ( Figure 1 ). Prediction performance did not change notably with inclusion of lesion volume information. This contrasted with the combination of functional lesion connectivity and lesion volume that achieved a substantially higher prediction performance compared to functional lesion connectivity or lesion volume in isolation. Lesion location resulted in a prediction performance that was in the range of lesion volume alone and significantly lower than the ones of structural or functional connectivity with lesion volume. Conclusions: Structural connectivity facilitated the most convincing prediction of stroke severity. The capacity of functional lesion connectivity was substantially improved by the inclusion of lesion volume, indicating both represent complementary information.

Introduction: Patients with baseline disability account for up to one-third of stroke presentations. Despite growing evidence supporting their benefit from endovascular thrombectomy (EVT), there remains controversy in treatment selection. We compared the long-term outcomes and the likelihood of comfort care for large vessel occlusion (LVO) stroke patients who presented with severe baseline disability treated with EVT versus medical management. Methods: Individuals who presented with LVO were identified from a prospectively maintained database from January 2017 to December 2020. Severe baseline disability was defined as modified Rankin Scale (mRS) 3-5. Delta mRS was defined as the difference between baseline and 90-day mRS. Logistic and ordinal regressions were performed to evaluate the relationships between EVT and outcomes. Results: A total of 175/1007 (17%) patients were identified with severe baseline disability. The median age was 82 (IQR 70-89), and 59% were female. Thirty-two (18%) patients with severe baseline disability were treated with EVT. EVT was independently associated with improved delta mRS (B=-1.048; 95%CI=-1.777,-0.318; p=0.005) when accounting for age and NIHSS. However, EVT did not reduce the odds of transitioning to comfort care (aOR=0.794; 95%CI=0.347,1.818; p=0.585) when accounting for age and NIHSS. Seventy-six (43%) patients with severe baseline disability were transitioned to comfort care. Of the 99 patients who were not transitioned to comfort care, 18 were treated with EVT. In this subgroup not transitioned to comfort care, EVT was independently associated with improved delta mRS (B=-2.794; 95%CI=-4.002,-1.586; p Conclusions: Among patients with severe baseline disability, EVT is associated with less post-stroke accumulated disability without limiting transitions to comfort care. EVT is compatible with goal-concordant care in patients with severe baseline disability; it should not be routinely withheld on the sole basis of baseline disability.

Introduction: Deep learning (DL) has allowed for substantial progress in many, often machine vision-focused medical scenarios. Since DL models typically require 10 5 -10 7 examples, it is currently unknown whether DL can also enhance predictions of outcomes post-stroke in real-world samples of stroke patients that are several magnitudes smaller. We here compared the capacities of linear and DL algorithms regarding their prediction of stroke severity. Methods: Our analyses relied on a total of 1435 patients assembled from the MRI-GENIE study and a Massachusetts General Hospital-based study. The primary outcome was acute NIHSS-based stroke severity, which we predicted by means of lesion location. We automatically derived lesion segmentations from DWI scans, performed spatial normalization and principal component analysis (PCA), retaining 95% of the variance of the original data. We then separated a train, validation, and test set, of sizes 918, 230, and 287 respectively. We downsampled the train set to 100, 300, and 900 and learned to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set and conducted 500 dataset splits. We here report explained variance (R-squared) in the test set. Results: While linear regression performed favorably for a sample size of 100 patients (R-squared=0.28), DL started to significantly outperform linear regression when trained on 900 patients (R-squared=0.34, Figure 1 ). Average prediction performance improved by ~20% when increasing the sample size 9x. Conclusions: For sample sizes of ~1000 patients, DL showed a more convincing prediction performance for stroke severity than typically employed linear methods. Our findings suggest the existence of non-linear relationships between lesion location and outcome post-stroke that can be utilized for an improved prediction performance once samples are reasonably large.

Introduction: Post-stroke cognitive impairment/dementia (PSCID) is highly prevalent and associated with poor long-term outcomes after acute ischemic stroke (AIS). Recognition of early determinants of PSCID allows for individualized interventions to reduce long-term disability. Furthermore, investigating the relation between PSCID and patient-reported outcomes provides insight into personalized impact of post-stroke cognitive dysfunction. We aimed to identify clinical determinants of PSCID and association with patient-reported outcome measures (PROMs) at 3- and 12-month after AIS. Methods: 138 AIS patients with no previous history of dementia were included. Clinical variables were acquired on hospital admission. Patients underwent a telephone interview at 3- and 12-months post-stroke including the Telephone Interview for Cognitive Status (TICS), modified Rankin scale (mRS), Barthel Index (BI) and PROM-10 questionnaires. PSCID was defined as TICS < 36 at 12-month post-stroke. Linear regression analyses were performed to identify predictors of 12-month TICS score and association with 3- and 12-month PROMs. Results: At 12 months post-stroke, 113 participants (82%) had PSCID. AIS patients with PSCID had higher rates of mRS ≥ 2 at 3-months (61.8% vs. 32%, p = 0.01), worse BI at 12-months (93.12 vs. 102, p < 0.01) and lower 12-month PROMs T Mental scores (48.22 vs. 52.41, p < 0.05). In linear regression analysis, worse functional and patient-reported outcomes at 3- and 12-months were independently associated with lower 12-month TICS (Table) . Conclusions: Large proportion of AIS patients experience PSCID at 12-months after stroke. 3-month PROMs, especially in the physical domain, are independently associated with worse cognitive measures at 12-months post-stroke. Moreover, PROMs at 12-months after AIS are strongly associated with PSCID. Incorporating PROMs in the ambulatory setting may offer additional insight into cognitive impairment after stroke.

Introduction: With continued expansion in indications for endovascular thrombectomy (EVT), understanding the pathophysiology of reperfusion injury and the risks of hemorrhagic transformation (HT) becomes increasingly important. Pre-EVT infarct topography may have implications for treatment decisions acutely (e.g. stenting), and with post EVT care (e.g. antithrombotics). We sought to quantify region-specific volumes of infarcted tissue on pre-EVT MRI, understand their importance for HT, and identify associations with clinical and imaging characteristics. Methods: Patients with pre-EVT MRI were identified from a prospectively maintained database. Each patient’s diffusion weighted sequence underwent manual infarct delineation and was registered to a standard space for overlay with cortical, subcortical, and white matter atlases. HT was defined as ECASS PH1 or PH2. Variables with p Results: A total of 165 participants [median age 69 (IQR 56-79), 56% women] were identified. Intravenous alteplase was administered to 52%; 70% achieved TICI 2b-3 reperfusion. HT occurred in 8%. The distribution of pre-EVT infarcts was 48% (38-60%) white matter, 23% (6-47%) cortex, and 15% (4-28%) basal ganglia. Pre-EVT infarct volumes [median (IQR)] were 22 cc (12-43 cc) for total, 11 cc (6-19 cc) for white matter, 5 cc (1-19 cc) for cortex, and 3 cc (1-6 cc) for basal ganglia infarct. Paramagnetic sequences showed 3% had petechial hemorrhage and 40% had susceptibility vessel sign. Basal ganglia infarct volume was independently associated with HT (OR=1.342, 95%CI=1.002,1.797) in a model including white matter infarct volume, cortex infarct volume, smoking, and puncture-to-recanalization time. Basal ganglia infarct volume was linked to susceptibility vessel sign (Beta=0.233, p=0.006) and NIHSS (Beta=0.220, p=0.012), when controlling for total infarct volume. Conclusions: Greater basal ganglia infarct volume was associated with a higher risk of HT when accounting for infarct volumes in other regions. Susceptibility vessel sign was associated with basal ganglia infarct volume, which may be related to acute middle cerebral artery perforator occlusion.

IntroductionDeep learning has allowed for remarkable progress in many medical scenarios. Since deep learning prediction models often require 105-107examples, it is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity.MethodsOur analyses relied on a total of 1,430 patients assembled from the MRI-GENIE collaboration and a Massachusetts General Hospital-based study. The outcome of interest was NIHSS-based stroke severity in the acute phase after ischemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis (PCA) step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation, and test set to investigate the effects of sample size, we subsampled the train set to 100, 300, and 900 and trained the algorithms to predict the NIHSS score for each sample size with regularized linear regression and an 8-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R-squared) in the test set.ResultsWhile linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9x (maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006).ConclusionsFor sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.

BACKGROUND AND PURPOSE Ischemic diffusion-weighted imaging-fluid-attenuated inversion recovery (DWI-FLAIR) mismatch may be useful in guiding acute stroke treatment decisions given its relationship to onset time and parenchymal viability; however, it relies on subjective grading. Radiomics is an emerging image quantification methodology that may objectively represent continuous image characteristics. We propose a novel radiomics approach to characterize DWI-FLAIR mismatch. METHODS Ischemic lesions were visually graded for FLAIR positivity (absent, subtle, obvious) among consecutive large vessel occlusion stroke patients who underwent hyperacute MRI. Radiomic features were extracted from within the lesions on DWI and FLAIR. The DWI-FLAIR mismatch radiomics signature was built with features systematically selected by a cross-validated ElasticNet linear regression model of mismatch. RESULTS We identified 103 patients with mean age 68 ± 16 years; 63% were female. FLAIR hyperintensity was absent in 25%, subtle in 55%, and obvious in 20%. Inter-rater agreement for visual grading was moderate (Κ = .58). The radiomics signature of DWI-FLAIR mismatch included native FLAIR histogram kurtosis and local binary pattern-filtered FLAIR gray-level cluster shade; both correlated with visual grading (ρ = -.42, p

Background For patients with large vessel occlusion (LVO) stroke, time to treatment with endovascular thrombectomy is crucial to prevent infarction and improve outcomes. We sought to evaluate the hub arrival‐to‐puncture times and outcomes for transferred patients accepted directly to the angio‐suite (LVO to operating room, LVO2OR) versus those accepted through the emergency department in a hub‐and‐spoke telestroke network. Methods Consecutive patients transferred for endovascular thrombectomy with spoke computed tomography angiography–confirmed LVO, spoke Alberta Stroke Program Early Computed Tomography score >6, and last known well–to–hub arrival Results The LVO2OR cohort was composed of 91 patients, and the emergency department cohort was composed of 90 patients. LVO2OR patients had more atrial fibrillation (51% versus 32%; P =0.02) and more M2 occlusions (27% versus 10%; P =0.01). LVO2OR patients had faster median hub arrival‐to‐puncture time (11 versus 92 minutes; P P P P =0.04). In a multivariable model, LVO2OR significantly increased the odds of 90‐day modified Rankin scale score P =0.04) even when controlling for age, baseline modified Rankin scale score, atrial fibrillation, National Institutes of Health Stroke Scale score, M2 occlusion location, and Thrombolysis in Cerebral Infarction score 2b to 3. Conclusions In a hub‐and‐spoke telestroke network, accepting transferred patients directly to the angio‐suite was associated with dramatically reduced hub arrival‐to‐puncture time and may lead to improved 90‐day outcomes. Direct–to–angio‐suite protocols should continue to be evaluated in other geographic regions and telestroke network models.

Background and ObjectivesTo examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern–specific ways.MethodsMR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3–6 months after stroke were obtained from the MRI–Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning–based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts.ResultsA total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location–specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location.DiscussionHigher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.