White matter hyperintensity on MRI and plasma Aß42/40 ratio additively increase the risk of cognitive impairment in hypertensive adults.

INTRODUCTION: Dementia often involves comorbid Alzheimer's and vascular pathology, but their combined impact warrants additional study. METHODS: We analyzed the Systolic Blood Pressure Intervention Trial and categorized white matter hyperintensity (WMH) volume into highest versus lowest/mid tertile and the amyloid beta (Aß)42/40 ratio into lowest versus mid/highest ratio tertile. Using these binary variables, we created four exposure categories: (1) combined low risk, (2) Aß risk, (3) WMH risk, and (4) combined high risk. RESULTS: In the cohort of 467 participants (mean age 69.7 ± 7.1, 41.8% female, 31.9% nonwhite or Hispanic) during 4.8 years of follow-up and across the four exposure categories the rates of cognitive impairment were 5.3%, 7.8%, 11.8%, and 22.6%. Compared to the combined low-risk category, the adjusted hazard ratio for cognitive impairment was 4.12 (95% confidence interval, 1.71 to 9.94) in the combined high-risk category. DISCUSSION: This study emphasizes the potential impact of therapeutic approaches to dementia prevention that target both vascular and amyloid pathology. HIGHLIGHTS: White matter hyperintensity (WMH) and plasma amyloid (Aß42/40) are additive risk factors for the development of cognitive impairment in the SPRINT MIND trial. Individuals in the high-risk categories of both WMH and Aß42/40 had a near fivefold increase in risk of cognitive impairment during 4.8 years of follow-up on average. These findings suggest that treatment strategies targeting both vascular health and amyloid burden warrant further research.

The predictive validity of a Brain Care Score for late-life depression and a composite outcome of dementia, stroke, and late-life depression: data from the UK Biobank cohort.

Introduction: The 21-point Brain Care Score (BCS) is a novel tool designed to motivate individuals and care providers to take action to reduce the risk of stroke and dementia by encouraging lifestyle changes. Given that late-life depression is increasingly recognized to share risk factors with stroke and dementia, and is an important clinical endpoint for brain health, we tested the hypothesis that a higher BCS is associated with a reduced incidence of future depression. Additionally, we examined its association with a brain health composite outcome comprising stroke, dementia, and late-life depression. Methods: The BCS was derived from the United Kingdom Biobank baseline evaluation in participants with complete data on BCS items. Associations of BCS with the risk of subsequent incident late-life depression and the composite brain health outcome were estimated using multivariable Cox proportional hazard models. These models were adjusted for age at baseline and sex assigned at birth. Results: A total of 363,323 participants were included in this analysis, with a median BCS at baseline of 12 (IQR: 11-14). There were 6,628 incident cases of late-life depression during a median follow-up period of 13 years. Each five-point increase in baseline BCS was associated with a 33% lower risk of incident late-life depression (95% CI: 29%-36%) and a 27% lower risk of the incident composite outcome (95% CI: 24%-30%). Discussion: These data further demonstrate the shared risk factors across depression, dementia, and stroke. The findings suggest that a higher BCS, indicative of healthier lifestyle choices, is significantly associated with a lower incidence of late-life depression and a composite brain health outcome. Additional validation of the BCS is warranted to assess the weighting of its components, its motivational aspects, and its acceptability and adaptability in routine clinical care worldwide.

Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.

Purpose: Computed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs. Methods: We split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission "CTA" images alone, "CTA + Treatment" (including time to thrombectomy and reperfusion success information), and "CTA + Treatment + Clinical" (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network ("MedicalNet") and included CTA preprocessing steps. Results: We generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59-0.81) for "CTA," 0.79 (0.70-0.89) for "CTA + Treatment," and 0.86 (0.79-0.94) for "CTA + Treatment + Clinical" input models. A "Treatment + Clinical" logistic regression model achieved an AUC of 0.86 (0.79-0.93). Conclusion: Our results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities.

APOE epsilon variants and composite risk of dementia, disability, and death in the health and retirement study.

BACKGROUND: Clinical trials in older adults are increasingly focused on functional outcomes, and the composite outcome of dementia, disability, and death is gaining pivotal importance. Genetic variation, particularly the APOE epsilon(ε) variants, may modify responses to new treatments. Although APOE ε4 is known to influence these outcomes separately, the magnitude of its effect on this composite outcome remains unknown. We tested the hypothesis that APOE ε4 increases, whereas APOE ε2 decreases, the risk of a composite outcome of dementia, disability, and death. METHODS: We evaluated clinical and genomic data from the Health and Retirement Study collected from 1992 to 2020. We used variants rs429358 and rs7412 to determine APOE genotypes, modeled dominantly (carriers/noncarriers). We conducted survival analysis, using multivariable Cox proportional hazards models with a composite endpoint of dementia, disability, and death. Our primary analysis evaluated participants with genetic data and no previous dementia or disability. In secondary analyses, we focused on persons aged > = 75 years without heart disease or stroke, a subpopulation increasingly important in clinical trials of older adults. RESULTS: We included 14,527 participants in the primary analysis. Over a median of 18 (Interquartile Range [IQR] 12-24) years, 6711 (46%) participants developed the composite outcome. In Cox analyses, APOE ε4 associated with higher risk (HR:1.15, 95%CI:1.09-1.22) of the composite outcome, whereas APOE ε2 associated with lower risk (HR:0.92, 95%CI:0.86-0.99). In the secondary analysis, we included 3174 participants. Over a median of 7 (IQR 4-11) years, 1326 participants (42%) developed the composite outcome. In Cox analyses, APOE ε4 associated with higher risk (HR:1.25, 95%CI:1.10-1.41) of the composite outcome, whereas APOE ε2 associated with lower risk (HR:0.84, 95%CI:0.71-0.98). CONCLUSIONS: APOE ε variants are linked to the risk of dementia, disability, and death in older adults. By examining these variants in clinical trials, we can better elucidate how they might alter the effectiveness of tested interventions. Importantly, this genetic information could help identify participants who may have greater absolute benefit from such interventions.

Bidirectional relationship between epigenetic age and brain health events.

Chronological age offers an imperfect estimate of the molecular changes that occur with aging. Epigenetic age, which is derived from DNA methylation data, provides a more nuanced representation of aging-related biological processes. This study examines the bidirectional relationship between epigenetic age and the occurrence of brain health events (stroke, dementia, and late-life depression). Using data from the Health and Retirement Study, we analyzed blood samples from over 4,000 participants to determine how epigenetic age relates to past and future brain health events. Study participants with a prior brain health event prior to blood collection were 4% epigenetically older (beta 0.04, SE 0.01), suggesting that these conditions are associated with faster aging than that captured by chronological age. Furthermore, a one standard deviation increase in epigenetic age was associated with 70% higher odds of experiencing a brain health event in the next four years after blood collection (OR 1.70, 95%CI 1.16-2.50), indicating that epigenetic age is not just a consequence but also a predictor of poor brain health. Both results were replicated through Mendelian Randomization analyses, supporting their causal nature. Our findings support the utilization of epigenetic age as a useful biomarker to evaluate the role of interventions aimed at preventing and promoting recovery after a brain health event.

Brain Care Score and Neuroimaging Markers of Brain Health in Asymptomatic Middle-Age Persons.

OBJECTIVES: To investigate associations between health-related behaviors as measured using the Brain Care Score (BCS) and neuroimaging markers of white matter injury. METHODS: This prospective cohort study in the UK Biobank assessed the BCS, a novel tool designed to empower patients to address 12 dementia and stroke risk factors. The BCS ranges from 0 to 21, with higher scores suggesting better brain care. Outcomes included white matter hyperintensities (WMH) volume, fractional anisotropy (FA), and mean diffusivity (MD) obtained during 2 imaging assessments, as well as their progression between assessments, using multivariable linear regression adjusted for age and sex. RESULTS: We included 34,509 participants (average age 55 years, 53% female) with no stroke or dementia history. At first and repeat imaging assessments, every 5-point increase in baseline BCS was linked to significantly lower WMH volumes (25% 95% CI [23%-27%] first, 33% [27%-39%] repeat) and higher FA (18% [16%-20%] first, 22% [15%-28%] repeat), with a decrease in MD (9% [7%-11%] first, 10% [4%-16%] repeat). In addition, a higher baseline BCS was associated with a 10% [3%-17%] reduction in WMH progression and FA decline over time. DISCUSSION: This study extends the impact of the BCS to neuroimaging markers of clinically silent cerebrovascular disease. Our results suggest that improving one's BCS could be a valuable intervention to prevent early brain health decline.

Correlations of Socioeconomic and Clinical Determinants with United States County-Level Stroke Prevalence.

Socioeconomic status (SES) is a multi-faceted theoretical construct associated with stroke risk and outcomes. Knowing which SES measures best correlate with population stroke metrics would improve its accounting in observational research and inform interventions. Using the Centers for Disease Control and Prevention's (CDC) Population Level Analysis and Community Estimates (PLACES) and other publicly available databases, we conducted an ecological study comparing correlations of different United States county-level SES, health care access and clinical risk factor measures with age-adjusted stroke prevalence. The prevalence of adults living below 150% of the federal poverty level most strongly correlated with stroke prevalence compared to other SES and non-SES measures (correlation coefficient = 0.908, R2 = 0.825; adjusted partial correlation coefficient: 0.589, R2 = 0.347). ANN NEUROL 2024.

Disability and Recurrent Stroke Among Participants in Stroke Prevention Trials.

Importance: Stroke secondary prevention trials have disproportionately enrolled participants with mild or no disability. The impact of this bias remains unclear. Objective: To investigate the association between poststroke disability and the rate of recurrent stroke during long-term follow up. Design, Setting, and Participants: This cohort study is a post hoc analysis of the Prevention Regimen For Effectively Avoiding Second Strokes (PRoFESS) and Insulin Resistance Intervention After Stroke (IRIS) secondary prevention clinical trial datasets. PRoFESS enrolled patients from 2003 to 2008, and IRIS enrolled patients from 2005 to 2015. Data were analyzed from September 23, 2023, to May 16, 2024. Exposure: The exposure was poststroke functional status at study baseline, defined as modified Rankin Scale (mRS; range, 0-5; higher score indicates more disability) score of 0 vs 1 to 2 vs 3 or greater. Main Outcomes and Measures: The primary outcome was recurrent stroke. The secondary outcome was major cardiovascular events (MACE), defined as recurrent stroke, myocardial infarction, new or worsening heart failure, or vascular death. Results: A total of 20 183 PRoFESS participants (mean [SD] age, 66.1 [8.5] years; 12 931 [64.1%] male) and 3265 IRIS participants (mean [SD] age, 62.7 [10.6] years; 2151 [65.9%] male) were included. The median (IQR) follow-up was 2.4 (1.9-3.0) years in PRoFESS and 4.7 (3.2-5.0) years in IRIS. In PRoFESS, the recurrent stroke rate was 7.2%, among patients with an mRS of 0, 8.7% among patients with an mRS of 1 or 2, and 10.6% among patients with an mRS of 3 or greater (χ22 = 27.1; P < .001); in IRIS the recurrent stroke rate was 6.4% among patients with an mRS of 0, 9.0% among patients with an mRS of 1 or 2, and 11.7% among patients with an mRS of 3 or greater (χ22 = 11.1; P < .001). The MACE rate was 10.1% among patients with an mRS of 0, 12.2% among patients with an mRS of 1 or 2, and 17.2% among patients with an mRS of 3 or greater (χ22 = 103.4; P < .001) in PRoFESS and 10.9% among patients with an mRS of 0, 13.3% among patients with an mRS of 1 or 2, and 15.3% among patients with an mRS of 3 or greater (χ22 = 5.8; P = .06) in IRIS. Compared with patients with an mRS of 0, patients with an mRS of 3 or greater had increased hazard for recurrent stroke in PRoFESS (hazard ratio [HR], 1.63; 95% CI, 1.38-1.92; P < .001) and in IRIS (HR, 1.91; 95% CI, 1.28-2.86; P = .002). There was also increased hazard for MACE in PRoFESS (HR, 1.90; 95% CI, 1.66-2.18; P < .001) and in IRIS (HR, 1.45; 95% CI, 1.03-2.03; P = .03). Conclusions and Relevance: This cohort study found that higher baseline poststroke disability was associated with increased rates of recurrent stroke and MACE. Including more patients with greater baseline disability in stroke prevention trials may improve the statistical power and generalizability of these studies.

Temperature Control Parameters Are Important: Earlier Preinduction Is Associated With Improved Outcomes Following Out-of-Hospital Cardiac Arrest.

STUDY OBJECTIVE: Temperature control trials in cardiac arrest patients have not reliably conferred neuroprotective benefit but have been limited by inconsistent treatment parameters. To evaluate the presence of a time dependent treatment effect, we assessed the association between preinduction time and clinical outcomes. METHODS: In this retrospective, single academic center study between 2014 and 2022, consecutive out-of-hospital cardiac arrest (OHCA) patients treated with temperature control were identified. Preinduction was defined as the time from hospital arrival to initiation of a closed-loop temperature feedback device [door to temperature control initiation time], and early door to temperature control device time was defined a priori as <3 hours. We assessed the association between good neurologic outcome (cerebral performance category 1 to 2) and door to temperature control device time using logistic regression. The proportion of patients who survived to hospital discharge was evaluated as a secondary outcome. A sensitivity analysis using inverse probability treatment weighting, created using a propensity score, was performed to minimize measurable confounding. RESULTS: Three hundred and forty-seven OHCA patients were included; the early door to temperature control device cohort included 75 (21.6%) patients with a median (interquartile range) door to temperature control device time of 2.50 (2.03 to 2.75) hours, whereas the late door to temperature control device cohort included 272 (78.4%) patients with a median (interquartile range) door to temperature control device time of 5.18 (4.19 to 6.41) hours. In the multivariable logistic regression model, early door to temperature control device time was associated with improved good neurologic outcome and survival before [adjusted odds ratio (OR) (95% confidence interval) 2.36 (1.16 to 4.81) and 3.02 (1.54 to 6.02)] and after [adjusted OR (95% confidence interval) 1.95 (1.19 to 3.79) and 2.14 (1.33 to 3.36)] inverse probability of treatment weighting, respectively. CONCLUSION: In our study of OHCA patients, a shorter preinduction time for temperature control was associated with improved good neurologic outcome and survival. This finding may indicate that early initiation in the emergency department will confer benefit. Our findings are hypothesis generating and need to be validated in future prospective trials.

Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion.

PURPOSE: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke. METHODS: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome. RESULTS: We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68-0.72], 0.70 [0.68-0.73], 0.55 [0.50-0.63], and 0.74 [0.73-0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57-0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome. CONCLUSIONS: Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader.

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework. METHODS: We used the admission non-contrast CT scans from 882 patients from the Massachusetts General Hospital ICH Study for training, hyperparameter optimization, and model selection, and 146 patients from the Yale New Haven ICH Study for external validation of a deep learning model predicting functional outcome. Disability (modified Rankin scale [mRS] > 2), severe disability (mRS > 4), and dependent living status were assessed via telephone interviews after 6, 12, and 24 months. The prediction methods were evaluated by the c-index and compared with ICH score and FUNC score. RESULTS: Using non-contrast CT, our deep learning model achieved higher prediction accuracy of post-ICH dependent living, disability, and severe disability by 6, 12, and 24 months (c-index 0.742 [95% CI -0.700 to 0.778], 0.712 [95% CI -0.674 to 0.752], 0.779 [95% CI -0.733 to 0.832] respectively) compared with the ICH score (c-index 0.673 [95% CI -0.662 to 0.688], 0.647 [95% CI -0.637 to 0.661] and 0.697 [95% CI -0.675 to 0.717]) and FUNC score (c-index 0.701 [95% CI- 0.698 to 0.723], 0.668 [95% CI -0.657 to 0.680] and 0.727 [95% CI -0.708 to 0.753]). In the external independent Yale-ICH cohort, similar performance metrics were obtained for disability and severe disability (c-index 0.725 [95% CI -0.673 to 0.781] and 0.747 [95% CI -0.676 to 0.807], respectively). Similar AUC of predicting each outcome at 6 months, 1 and 2 years after ICH was achieved compared with ICH score and FUNC score. CONCLUSION: We developed a generalizable deep learning model to predict onset of dependent living and disability after ICH, which could help to guide treatment decisions, advise relatives in the acute setting, optimize rehabilitation strategies, and anticipate long-term care needs.

Mapping the Ecological Terrain of Stroke Prehospital Delay: A Nationwide Registry Study.

BACKGROUND: Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. METHODS: We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. RESULTS: Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93-0.98]) and 4 (aHR, 0.93 [95% CI, 0.91-0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15-16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community's socioeconomic status (aHR, 0.80 [95% CI, 0.74-0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84-0.94]). CONCLUSIONS: This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

Twenty Years of Get With The Guidelines-Stroke: Celebrating Past Successes, Lessons Learned, and Future Challenges.

The Get With The Guidelines-Stroke program which, began 20 years ago, is one of the largest and most important nationally representative disease registries in the United States. Its importance to the stroke community can be gauged by its sustained growth and widespread dissemination of findings that demonstrate sustained increases in both the quality of care and patient outcomes over time. The objectives of this narrative review are to provide a brief history of Get With The Guidelines-Stroke, summarize its major successes and impact, and highlight lessons learned. Looking to the next 20 years, we discuss potential challenges and opportunities for the program.

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records.

Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists' review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists' diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier's diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Diffusion-Weighted Imaging Fluid-Attenuated Inversion Recovery Mismatch on Portable, Low-Field Magnetic Resonance Imaging Among Acute Stroke Patients.

OBJECTIVE: For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. METHODS: Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. RESULTS: The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. INTERPRETATION: A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024;96:321-331.

Use of a dedicated open transcarotid access system for neurovascular disease.

BACKGROUND: Transcarotid artery revascularization (TCAR) is an increasingly popular technique for the management of extracranial carotid stenosis. Its off-label use in the treatment of intracranial neurovascular disease is poorly described. Our objective is to describe the use of a dedicated open transcarotid access system for the treatment of neurovascular pathologies other than extracranial carotid stenosis. METHODS: We conducted a retrospective review of a prospectively maintained database of consecutive patients who underwent treatment of neurovascular disease at a single academic center using the ENROUTE Transcarotid Arterial Sheath. Demographics, procedural characteristics, and patient outcomes were reported. RESULTS: Twenty patients were included in the study between September 2017 and March 2023. The following pathologies were treated: intracranial atherosclerotic disease (ICAD, nine patients), complex cervico-petrous carotid disease (five patients), intracranial aneurysms (three patients), and large vessel occlusion-acute ischemic stroke (three patients). Eighteen of the 20 cases were performed with active carotid flow reversal. All cases were successfully completed. There were no access-related complications. One periprocedural complication was incurred: a microguidewire perforation during an exchange maneuver for the treatment of ICAD. CONCLUSION: An open transcarotid approach using a dedicated transcarotid system may offer a safe alternative access strategy for the endovascular treatment of complex neurovascular pathologies when a traditional transfemoral or transradial approach is contraindicated or failed.

Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival.

The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan-Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the "first-order energy" radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p < 0.0001), alongside age, National Institutes of Health Stroke Scale (NIHSS), and baseline International Normalized Ratio (INR). Using a Receiver Operating Characteristic (ROC) analysis, "the first-order energy" was a predictor of mortality at 1-week, 1-month, and 3-month post-ICH (all p < 0.0001), with Area Under the Curves (AUC) of >0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher "first-order energy" or very bright hematomas are associated with worse survival outcomes.