The relevance of rich club regions for functional outcome post-stroke is enhanced in women.

This study aimed to investigate the influence of stroke lesions in predefined 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 enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. 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. 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 1000 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 effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. 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. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence.

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and continuously improving treatment options such as thrombolysis and thrombectomy have revolutionized acute stroke treatment in recent years. Following modern rhythms, the next revolution might well be the strategic use of the steadily increasing amounts of patient-related data for generating models enabling individualized outcome predictions. Milestones have already been achieved in several health care domains, as big data and artificial intelligence have entered everyday life. The aim of this review is to synoptically illustrate and discuss how artificial intelligence approaches may help to compute single-patient predictions in stroke outcome research in the acute, subacute and chronic stage. We will present approaches considering demographic, clinical and electrophysiological data, as well as data originating from various imaging modalities and combinations thereof. We will outline their advantages, disadvantages, their potential pitfalls and the promises they hold with a special focus on a clinical audience. Throughout the review we will highlight methodological aspects of novel machine-learning approaches as they are particularly crucial to realize precision medicine. We will finally provide an outlook on how artificial intelligence approaches might contribute to enhancing favourable outcomes after stroke.

Recovery after stroke: the severely impaired are a distinct group.

INTRODUCTION: Stroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently. METHODS: We designed a Bayesian hierarchical model to estimate 3-6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5-30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range. RESULTS: Recovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3-6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%). CONCLUSIONS: Our work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

Female Stroke: Sex Differences in Acute Treatment and Early Outcomes of Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Men and women are differently affected by acute ischemic stroke (AIS) in many aspects. Prior studies on sex disparities were limited by moderate sample sizes, varying years of data acquisition, and inconsistent inclusions of covariates leading to controversial findings. We aimed to analyze sex differences in AIS severity, treatments, and early outcome and to systematically evaluate the effect of important covariates in a large German stroke registry. METHODS: Analyses were based on the Stroke Registry of Northwestern Germany from 2000 to 2018. We focused on admission-stroke severity and disability, acute recanalization treatment, and early stroke outcomes. Potential sex divergences were investigated via odds ratio (OR) using logistic regression models. Covariates were introduced in 3 steps: (1) base models (age and admission year), (2) partially adjusted models (additionally corrected for acute stroke severity and recanalization treatment), (3) fully adjusted models (additionally adjusted for onset-to-admission time interval, prestroke functional status, comorbidities, and stroke cause). Models were separately fitted for the periods 2000 to 2009 and 2010 to 2018. RESULTS: Data from 761 106 patients with AIS were included. In fully adjusted models, there were no sex differences with respect to treatment with intravenous thrombolysis (2000-2009: OR, 0.99 [95% CI, 0.94-1.03]; 2010-2018: OR, 1.0 [0.98-1.02]), but women were more likely to receive intraarterial therapy (2010-2018: OR, 1.12 [1.08-1.15]). Despite higher disability on admission (2000-2009: OR, 1.10 [1.07-1.13]; 2010-2018: OR, 1.09 [1.07-1.10]), female patients were more likely to be discharged with a favorable functional outcome (2003-2009: OR, 1.05 [1.02-1.09]; 2010-2018: OR, 1.05 [1.04-1.07]) and experienced lower in-hospital mortality (2000-2009: OR, 0.92 [0.86-0.97]; 2010-2018: OR, 0.91 [0.88-0.93]). CONCLUSIONS: Female patients with AIS have a higher chance of receiving intraarterial treatment that cannot be explained by clinical characteristics, such as age, premorbid disability, stroke severity, or cause. Women have a more favorable in-hospital recovery than men because their higher disability upon admission was followed by a lower in-hospital mortality and a higher likelihood of favorable functional outcome at discharge after adjustment for covariates.

Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke.

The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.

Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states.

Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.

Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment.

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies-one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called 'fitters', pointed to a combination of proportional to lost function and constant recovery. 'Proportional to lost' here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, 'fitters' and 'non-fitters', a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

Veridical stimulus localization is linked to human area V5/MT+ activity.

How the brain represents visual space is an unsolved mystery. Spatial localization becomes particularly challenging when visual information processing is briefly disrupted, as in the case of saccadic eye movements, blinks, or visual masks. As we have recently reported, a compression of visual space, illustrated by displacements of shortly flashed stimuli, can be observed in the temporal vicinity of masking stimuli during ocular fixation (Zimmermann et al., 2013). We here aimed at investigating the neural mechanisms underlying these displacements using functional magnetic resonance imaging. On the behavioral level, we detected significant stimulus displacement when visual masks were simultaneously presented. At the neural level, we observed decreased human motion complex V5/MT+ activation associated with these displacements: When comparing trials with a perceived stimulus shift in space to trials of veridical perception of stimulus localization, human V5/MT+ was significantly less activated although no differences in perceived motion can account for this. Data suggest an important role of human V5/MT+ in the process of spatial localization of briefly presented objects and thus extend current concepts of the functions of human V5/MT+.

Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.

Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. 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. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic 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 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 stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While 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 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for 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.

Characterizing coma in large vessel occlusion stroke.

BACKGROUND: Coma is an unresponsive state of disordered consciousness characterized by impaired arousal and awareness. 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 retrospectively 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. RESULTS: 28/637 (4.4%) patients with LVO stroke were identified as presenting with coma. The median NIHSS was 32 (IQR 29-34) for those with coma versus 11 (5-18) for those without (p < 0.0001). In coma, occlusion locations included basilar (13), vertebral (2), internal carotid (5), and middle cerebral (9) arteries. 8/28 were treated with endovascular thrombectomy (EVT), and 20/28 died during the admission. 65% of patients not treated with EVT had delayed presentations or large established infarcts. In models accounting for pre-stroke mRS, basilar occlusion location, intravenous thrombolysis, and EVT, coma independently increased the odds of transitioning to comfort care during admission (aOR 6.75; 95% CI 2.87,15.84; p < 0.001) and decreased the odds of 90-day mRS 0-2 (aOR 0.12; 95% CI 0.03,0.55; p = 0.007). CONCLUSIONS: It is not uncommon for patients with LVO to present with coma, and delayed recognition of LVO can lead to poor outcomes, 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. Efforts to improve early diagnosis and care of patients with LVO presenting with coma are crucial.

Altered Dynamic Resting State Functional Connectivity Associated With Somatosensory Impairments in the Upper Limb in the Early Sub-Acute Phase Post-Stroke.

BACKGROUND.: Altered dynamic functional connectivity has been associated with motor impairments in the acute phase post-stroke. Its association with somatosensory impairments in the early sub-acute phase remains unexplored. OBJECTIVE.: To investigate altered dynamic functional connectivity associated with somatosensory impairments in the early sub-acute phase post-stroke. METHODS.: We collected resting state magnetic resonance imaging and clinical somatosensory function of the upper limb of 20 subacute stroke patients and 16 healthy controls (HC). A sliding-window approach was used to identify 3 connectivity states based on the estimated dynamic functional connectivity of sensorimotor related networks. Network components were subdivided into 3 domains: cortical and subcortical sensorimotor, as well as cognitive control network. Between-group differences were investigated using independent t-tests and Mann-Whitney-U tests. Analyzes were performed with correction for age, head motion and time post-stroke and corrected for multiple comparisons. RESULTS.: Stroke patients spent significantly less time in a weakly connected network state (state 3; dwell time: pstate3 = 0.003, meanstroke = 53.02, SDstroke = 53.13; meanHC = 118.92, SDHC = 72.84), and stayed shorter but more time intervals in a highly connected intra-domain network state (state 1; fraction time: pstate 1 < 0.001, meanstroke = 0.46, SDstroke = 0.26; meanHC = 0.26, SDHC = 0.21) compared to HC. After 8 weeks of therapy, improvements in wrist proprioception were moderately associated with decreases in dwell and fraction times toward a more normalized pattern. CONCLUSION.: Changes in temporal properties of large-scale network interactions are present in the early rehabilitation phase post-stroke and could indicate enhanced neural plasticity. These findings could augment the understanding of cerebral reorganization after loss of neural tissue specialized in somatosensory functions.

Fronto-striatal dynamic connectivity is linked to dopaminergic motor response in Parkinson's disease.

INTRODUCTION: Differences in dopaminergic motor response in Parkinson's disease (PD) patients can be related to PD subtypes, and previous fMRI studies associated dopaminergic motor response with corticostriatal functional connectivity. While traditional fMRI analyses have assessed the mean connectivity between regions of interest, an important aspect driving dopaminergic response might lie in the temporal dynamics in corticostriatal connections. METHODS: This study aims to determine if altered resting-state dynamic functional network connectivity (DFC) is associated with dopaminergic motor response. To test this, static and DFC were assessed in 32 PD patients and 18 healthy controls (HC). Patients were grouped as low and high responders using a median split of their dopaminergic motor response. RESULTS: Patients featuring a high dopaminergic motor response were observed to spend more time in a regionally integrated state compared to HC. Furthermore, DFC between the anterior midcingulate cortex/dorsal anterior cingulate cortex (aMCC/dACC) and putamen was lower in low responders during a more segregated state and correlated with dopaminergic motor response. CONCLUSION: The findings of this study revealed that temporal dynamics of fronto-striatal connectivity are associated with clinically relevant information, which may be considered when assessing functional connectivity between regions involved in motor initiation.

Neuroimaging markers of patient-reported outcome measures in acute ischemic stroke.

Objectives: To determine the relationship between patient-reported outcome measures (PROMs) and volumetric imaging markers in acute ischemic stroke (AIS). Patients and Methods: Patients presenting at Massachusetts General Hospital between February 14, 2017 and February 5, 2020 with a confirmed AIS by MRI were eligible and underwent a telephone interview including PROM-10 questionnaires 3-15 months after stroke. White matter hyperintensity (VWMH) and brain volumes (VBrain) were automatically determined using admission clinical MRI. Stroke lesions were manually segmented and volumes calculated (VLesion). Multivariable and ordinal regression analyses were performed to identify associations between global and PROM-10 subscores with brain volumetrics and clinical variables. Results: Utilizing data from 167 patients (mean age: 64.7; 41.9% female), higher VWMH was associated with worse global physical (ß=-0.6), global mental (ß=-0.65), physical health (OR=0.68), social satisfaction (OR=0.66), fatigue (OR=0.69) and social activities (OR=0.59) scores. Higher VLesion was associated with poorer global mental (ß=-0.79), mental health (OR=0.68), physical (OR=0.66) and social activities (OR=0.55), and emotional distress (OR=0.68) scores. Higher VBrain was linked to better global mental (ß=0.93), global physical (ß=0.79), mental health (OR=1.54) and physical activities (OR=1.72) scores. Conclusions: Neuroimaging biomarkers were significantly associated with PROMs, where higher VWMH and VLesion led to worse outcome, while higher VBrain was protective. The inclusion of neuroimaging analyses and PROMs in routine assessment provides enhanced understanding of post-stroke outcomes.

In a hub-and-spoke network, spoke-administered thrombolysis reduces mechanical thrombectomy procedure time and number of passes.

BACKGROUND: The utility of intravenous thrombolysis (IVT) prior to mechanical thrombectomy (MT) in large vessel occlusion stroke (LVO) is controversial. Some data suggest IVT increases MT technical difficulty. Within our hub-and-spoke telestroke network, we examined how spoke-administered IVT affected hub MT procedure time and pass number. METHODS: Patients presenting to 25 spoke hospitals who were transferred to the hub and underwent MT from 2018 to 2020 were identified from a prospectively maintained database. MT procedure time, fluoroscopy time, and pass number were obtained from operative reports. RESULTS: Of 107 patients, 48 received IVT at spokes. Baseline characteristics and NIHSS were similar. The last known well (LKW)-to-puncture time was shorter among IVT patients (4.3 ± 1.9 h vs. 10.5 ± 6.5 h, p < 0.0001). In patients that received IVT, mean MT procedure time was decreased by 18.8 min (50.5 ± 29.4 vs. 69.3 ± 46.7 min, p = 0.02) and mean fluoroscopy time was decreased by 11.3 min (21.7 ± 15.8 vs. 33.0 ± 30.9 min, p = 0.03). Furthermore, IVT-treated patients required fewer MT passes (median 1 pass [IQR 1.0, 1.80] vs. 2 passes [1.0, 2.3], p = 0.0002) and were more likely to achieve reperfusion in ≤2 passes (81.3% vs. 59.3%, p = 0.01). An increased proportion of IVT-treated patients achieved TICI 2b-3 reperfusion after MT (93.9% vs. 83.8%, p = 0.045). There were no associations between MT procedural characteristics and LKW-to-puncture time. CONCLUSION: Within our network, hub MT following spoke-administered IVT was faster, required fewer passes, and achieved improved reperfusion. This suggests spoke-administered IVT does not impair MT, but instead may enhance it.

Spoke-administered thrombolysis improves large vessel occlusion early recanalization: the real-world experience of a large academic hub-and-spoke telestroke network.

Introduction: Intravenous thrombolysis (IVT) prior to mechanical thrombectomy (MT) for large vessel occlusion (LVO) stroke is increasingly controversial. Recent trials support MT without IVT for patients presenting directly to MT-capable "hub" centers. However, bypassing IVT has not been evaluated for patients presenting to IVT-capable "spoke" hospitals that require hub transfer for MT. A perceived lack of efficacy of IVT to result in LVO early recanalization (ER) is often cited to support bypassing IVT, but ER data for IVT in patients that require interhospital transfer is limited. Here we examined LVO ER rates after spoke-administered IVT in our hub-and-spoke stroke network. Methods: Patients presenting to 25 spokes before hub transfer for MT consideration from 2018-2020 were retrospectively identified from a prospectively maintained database. Inclusion criteria were pre-transfer CTA-defined LVO, ASPECTS ≥6, and post-transfer repeat vessel imaging. Results: Of 167 patients, median age was 69 and 51% were female. 76 received spoke IVT (+spokeIVT) and 91 did not (-spokeIVT). Alteplase was the only IVT used in this study. Comorbidities and NIHSS were similar between groups. ER frequency was increased 7.2-fold in +spokeIVT patients [12/76 (15.8%) vs. 2/91 (2.2%), P<0.001]. Spoke-administered IVT was independently associated with ER (aOR=11.5, 95% CI=2.2,99.6, p<0.05) after adjusting for timing of last known well, interhospital transfer, and repeat vessel imaging. Interval NIHSS was improved in patients with ER (median -2 (IQR -6.3, -0.8) vs. 0 (-2.5, 1), p<0.05). Conclusion: Within our network, +spokeIVT patients had a 7.2-fold increased ER relative likelihood. This real-world analysis supports IVT use in eligible patients with LVO at spoke hospitals before hub transfer for MT.

Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES: While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes. METHODS: We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input. RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION: T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Development and Validation of Prediction Models for Severe Complications After Acute Ischemic Stroke: A Study Based on the Stroke Registry of Northwestern Germany.

Background The treatment of stroke has been undergoing rapid changes. As treatment options progress, prediction of those under risk for complications becomes more important. Available models have, however, frequently been built based on data no longer representative of today's care, in particular with respect to acute stroke management. Our aim was to build and validate prediction models for 4 clinically important, severe outcomes after stroke. Methods and Results We used German registry data from 152 710 patients with acute ischemic stroke obtained in 2016 (development) and 2017 (validation). We took into account potential predictors that were available at admission and focused on in-hospital mortality, intracranial mass effect, secondary intracerebral hemorrhage, and deep vein thrombosis as outcomes. Validation cohort prediction and calibration performances were assessed using the following 4 statistical approaches: logistic regression with backward selection, l1-regularized logistic regression, k-nearest neighbor, and gradient boosting classifier. In-hospital mortality and intracranial mass effects could be predicted with high accuracy (both areas under the curve, 0.90 [95% CI, 0.90-0.90]), whereas the areas under the curve for intracerebral hemorrhage (0.80 [95% CI, 0.80-0.80]) and deep vein thrombosis (0.73 [95% CI, 0.73-0.73]) were considerably lower. Stroke severity was the overall most important predictor. Models based on gradient boosting achieved better performances than those based on logistic regression for all outcomes. However, area under the curve estimates differed by a maximum of 0.02. Conclusions We validated prediction models for 4 severe outcomes after acute ischemic stroke based on routinely collected, recent clinical data. Model performance was superior to previously proposed approaches. These predictions may help to identify patients at risk early after stroke and thus facilitate an individualized level of care.

Understanding Delays in MRI-based Selection of Large Vessel Occlusion Stroke Patients for Endovascular Thrombectomy.

PURPOSE: Given the efficacy of endovascular thrombectomy (EVT), optimizing systems of delivery is crucial. Magnetic resonance imaging (MRI) is the gold standard for evaluating tissue viability but may require more time to obtain and interpret. We sought to identify determinants of arrival-to-puncture time for patients who underwent MRI-based EVT selection in a real-world setting. METHODS: Patients were identified from a prospectively maintained database from 2011-2019 that included demographics, presentations, treatments, and outcomes. Process times were obtained from the medical charts. MRI times were obtained from time stamps on the first sequence. Linear and logistic regressions were used to infer explanatory variables of arrival-to-puncture times and effects of arrival-to-puncture time on functional outcomes. RESULTS: In this study 192 patients (median age 70 years, 57% women, 12% non-white) underwent MRI-based EVT selection. 66% also underwent computed tomography (CT) at the hub before EVT. General anesthesia was used for 33%. Among the entire cohort, the median arrival-to-puncture was 102 min; however, among those without CT it was 77 min. Longer arrival-to-puncture times independently reduced the odds of 90-day good outcome (∆mRS ≤ 2 from pre-stroke, aOR = 0.990, 95%CI = 0.981-0.999, p = 0.040) when controlling for age, NIHSS, and good reperfusion (TICI 2b-3). Independent determinants of longer arrival-to-puncture were CT plus MRI (ß = 0.205, p = 0.003), non-white race/ethnicity (ß = 0.162, p = 0.012), coronary disease (ß = 0.205, p = 0.001), and general anesthesia (ß = 0.364, p < 0.0001). CONCLUSION: Minimizing arrival-to-puncture time is important for outcomes. Real-world challenges exist in an MRI-based EVT selection protocol; avoiding double imaging is key to saving time. Racial/ethnic disparities require further study. Understanding variables associated with delay will inform protocol changes.

Association of Infarct Topography and Outcome After Endovascular Thrombectomy in Patients With Acute Ischemic Stroke.

BACKGROUND AND OBJECTIVES: The 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. METHODS: For 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. RESULTS: There 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). DISCUSSION: These 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.

Direct to angio-suite large vessel occlusion transfers achieve faster arrival-to-puncture times and improved outcomes.

Introduction: For patients with large vessel occlusion (LVO) stroke, time to treatment with endovascular thrombectomy (EVT) 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 (LVO2OR) versus those accepted through the emergency department (ED) in a hub-and-spoke telestroke network. Methods: Consecutive patients transferred for EVT with spoke CTA-confirmed LVO, spoke ASPECTS >6, and LKW-to-hub arrival <6 hours were identified. Our LVO2OR protocol began implementation in January 2017. The LVO2OR cohort includes patients who underwent EVT from July 2017 to October 2020; the ED cohort includes those from January 2011 to December 2016. Hub arrival-to-puncture time and 90-day modified Rankin Scale (mRS) were prospectively recorded. Results: The LVO2OR cohort was comprised of 91 patients and the ED cohort 90. LVO2OR patients had more atrial fibrillation (AF, 51% vs 32%, p=0.02) and more M2 occlusions (27% vs 10%, p=0.01). LVO2OR patients had faster median hub arrival-to-puncture time (11 vs 92 minutes, p<0.001), faster median telestroke consult-to-puncture time (2.4 vs 3.6 hours, p<0.001), greater TICI 2b-3 reperfusion (92% vs 69%, p<0.001), and greater 90-day mRS <2 (35% vs 21%, p=0.04). In a multivariable model, LVO2OR significantly increased the odds of 90-day mRS <2 (aOR 2.77, 95%CI 1.07,7.20; p=0.04) even when controlling for age, baseline mRS, AF, NIHSS, M2 location, and TICI 2b-3. Conclusion: 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 regions and telestroke models.