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.

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.

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.

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.

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.

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.

Contemporary Prestroke Dual Antiplatelet Use and Symptomatic Intracerebral Hemorrhage Risk After Thrombolysis.

Importance: Intravenous alteplase (IV-tPA) can be administered to patients with acute ischemic stroke but is associated with symptomatic intracerebral hemorrhage (sICH). It is unclear if patients taking prestroke dual antiplatelet therapy (DAPT) are at higher risk of sICH. Objective: To determine the associated risk of sICH in patients taking prestroke dual antiplatelet therapy receiving alteplase for acute ischemic stroke using propensity score matching analysis. Design, Setting, and Participants: This cohort study used data from the American Heart Association and American Stroke Association Get With The Guidelines-Stroke (GWTG-Stroke) registry between 2013 and 2021. Data were obtained from hospitals in the GWTG-Stroke registry. This study included patients hospitalized with acute ischemic stroke and treated with IV-tPA. Data were analyzed from January 2013 to December 2021. Exposures: Prestroke DAPT before treatment with IV-tPA for acute ischemic stroke. Main Outcome Measures: sICH, In-hospital death, discharge modified Rankin scale score, and other life-threatening systemic hemorrhages. Results: Of 409 673 participants, 321 819 patients (mean [SD] age, 68.6 [15.1] years; 164 587 female [51.1%]) who were hospitalized with acute ischemic stroke and treated with IV-tPA were included in the analysis. The rate of sICH was 2.9% (5200 of 182 344), 3.8% (4457 of 117 670), and 4.1% (893 of 21 805) among patients treated with no antiplatelet therapy, single antiplatelet therapy (SAPT), and DAPT, respectively (P < .001). In adjusted analyses after propensity score subclassification, both SAPT (odds ratio [OR], 1.13; 95% CI, 1.07-1.19) and DAPT (OR, 1.28; 95% CI, 1.14-1.42) were associated with increased risks of sICH. Prestroke antiplatelet medications were associated with lower odds of discharge mRS score of 2 or less compared with no medication (SAPT OR, 0.92; 95% CI, 0.90-0.95; DAPT OR, 0.94; 95% CI, 0.88-0.98). Results of a subgroup analysis of patients taking DAPT exposed to aspirin-clopidogrel vs aspirin-ticagrelor combination therapy were not significant (OR, 1.35; 95% CI, 0.84-1.86). Conclusions and Relevance: Prestroke DAPT was associated with a significantly elevated risk of sICH among patients with ischemic stroke who were treated with thrombolysis; however, the absolute increase in risk was small. Patients exposed to antiplatelet medications did not have excess sICH compared with landmark trials, which demonstrated overall clinical benefit of thrombolysis therapy for acute ischemic stroke.

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.

Secondary Prevention in Patients With Stroke Versus Myocardial Infarction: Analysis of 2 National Cohorts.

BACKGROUND: The implementation of preventive therapies among patients with stroke remains inadequately explored, especially when compared with patients with myocardial infarction (MI), despite sharing similar vascular risk profiles. We tested the hypothesis that participants with a history of stroke have a worse cardiovascular prevention profile in comparison to participants with MI. METHODS AND RESULTS: In cross-sectional analyses within the UK Biobank and All of Us Research Program, involving 14 760 (9193 strokes, 5567 MIs) and 7315 (2948 strokes, 4367 MIs) participants, respectively, we evaluated cardiovascular prevention profiles assessing low-density lipoprotein (<100 mg/dL), blood pressure (systolic, <140 mm Hg; and diastolic, <90 mm Hg), statin and antiplatelet use, and a cardiovascular prevention score that required meeting at least 3 of these criteria. The results revealed that, within the UK Biobank, patients with stroke had significantly lower odds of meeting all the preventive criteria compared with patients with MI: low-density lipoprotein control (odds ratio [OR], 0.73 [95% CI, 0.68-0.78]; P<0.001), blood pressure control (OR, 0.63 [95% CI, 0.59-0.68]; P<0.001), statin use (OR, 0.45 [95% CI, 0.42-0.48]; P<0.001), antiplatelet therapy use (OR, 0.30 [95% CI, 0.27-0.32]; P<0.001), and cardiovascular prevention score (OR, 0.42 [95% CI, 0.39-0.45]; P<0.001). Similar patterns were observed in the All of Us Research Program, with significant differences across all comparisons (P<0.05), and further analysis suggested that the odds of having a good cardiovascular prevention score were influenced by race and ethnicity as well as neighborhood deprivation levels (interaction P<0.05 in both cases). CONCLUSIONS: In 2 independent national cohorts, patients with stroke showed poorer cardiovascular prevention profiles and lower adherence to guideline-directed therapies compared with patients with MI. These findings underscore the need to explore the reasons behind the underuse of secondary prevention in vulnerable stroke survivors.

Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.

BACKGROUND: A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics. METHODS: We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy. RESULTS: We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort. CONCLUSIONS: Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.

APOE ε4 and Intracerebral Hemorrhage in Patients With Brain Arteriovenous Malformation.

Importance: Intracerebral hemorrhage (ICH) is a serious complication of brain arteriovenous malformation (AVM). Apolipoprotein E (APOE) ε4 is a well-known genetic risk factor for ICH among persons without AVM, and cerebral amyloid angiopathy is a vasculopathy frequently observed in APOE ε4 carriers that may increase the risk of ICH. Objective: To assess whether APOE ε4 is associated with a higher risk of ICH in patients with a known AVM. Design, Setting, and Participants: This cross-sectional study including 412 participants was conducted in 2 stages (discovery and replication) using individual-level data from the UK Biobank (released March 2012 and last updated October 2023) and the All of Us Research Program (commenced on May 6, 2018, with its latest update provided in October 2023). The occurrence of AVM and ICH was ascertained at the time of enrollment using validated International Classification of Diseases, Ninth Revision and Tenth Revision, codes. Genotypic data on the APOE variants rs429358 and rs7412 were used to ascertain the ε status. Main Outcomes and Measures: For each study, the association between APOE ε4 variants and ICH risk was assessed among patients with a known AVM by using multivariable logistic regression. Results: The discovery phase included 253 UK Biobank participants with known AVM (mean [SD] age, 56.6 [8.0] years, 119 [47.0%] female), of whom 63 (24.9%) sustained an ICH. In the multivariable analysis of 240 participants of European ancestry, APOE ε4 was associated with a higher risk of ICH (odds ratio, 4.58; 95% CI, 2.13-10.34; P < .001). The replication phase included 159 participants with known AVM enrolled in All of Us (mean [SD] age, 57.1 [15.9] years; 106 [66.7%] female), of whom 29 (18.2%) sustained an ICH. In multivariable analysis of 101 participants of European ancestry, APOE ε4 was associated with higher risk of ICH (odds ratio, 4.52; 95% CI, 1.18-19.38; P = .03). Conclusions and Relevance: The results of this cross-sectional study of patients from the UK Biobank and All of Us suggest that information on APOE ε4 status may help identify patients with brain AVM who are at particularly high risk of ICH and that cerebral amyloid angiopathy should be evaluated as a possible mediating mechanism of the observed association.

Association Between Hematoma Volume and Risk of Subsequent Ischemic Stroke: A MISTIE III and ATACH-2 Analysis.

BACKGROUND: Nontraumatic intracerebral hemorrhage (ICH) is independently associated with a long-term increased risk of major arterial ischemic events. While the relationship between ICH location and ischemic risk has been studied, whether hematoma volume influences this risk is poorly understood. METHODS: We pooled individual patient data from the MISTIE III (Minimally Invasive Surgery Plus Alteplase for Intracerebral Hemorrhage Evacuation Phase 3) and the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage-2) trials. The exposure was hematoma volume, treated as a continuous measure in the primary analysis, and dichotomized by the median in the secondary analyses. The outcome was a symptomatic, clinically overt ischemic stroke, adjudicated centrally within each trial. We evaluated the association between hematoma volume and the risk of an ischemic stroke using Cox regression analyses after adjustment for demographics, vascular comorbidities, and ICH characteristics. RESULTS: Of 1470 patients with ICH, the mean age was 61.7 (SD, 12.8) years, and 574 (38.3%) were female. The median hematoma volume was 17.3 mL (interquartile range, 7.2-35.7). During a median follow-up of 107 days (interquartile range, 91-140), a total of 30 ischemic strokes occurred, of which 22 were in patients with a median ICH volume of ≥17.3 mL and a cumulative incidence of 4.6% (95% CI, 3.1-7.1). Among patients with a median ICH volume <17.3 mL, there were 8 ischemic strokes with a cumulative incidence of 3.1% (95% CI, 1.7-6.0). In primary analyses using adjusted Cox regression models, ICH volume was associated with an increased risk of ischemic stroke (hazard ratio, 1.02 per mL increase [95% CI, 1.01-1.04]). In secondary analyses, ICH volume of ≥17.3 mL was associated with an increased risk of ischemic stroke (hazard ratio, 2.5 [95% CI, 1.1-7.2]), compared with those with an ICH volume <17.3 mL. CONCLUSIONS: In a heterogeneous cohort of patients with ICH, initial hematoma volume was associated with a heightened short-term risk of ischemic stroke.

Time-Dependent Changes in Hematoma Expansion Rate after Supratentorial Intracerebral Hemorrhage and Its Relationship with Neurological Deterioration and Functional Outcome.

BACKGROUND: Hematoma expansion (HE) following an intracerebral hemorrhage (ICH) is a modifiable risk factor and a treatment target. We examined the association of HE with neurological deterioration (ND), functional outcome, and mortality based on the time gap from onset to baseline CT. METHODS: We included 567 consecutive patients with supratentorial ICH and baseline head CT within 24 h of onset. ND was defined as a ≥4-point increase on the NIH stroke scale (NIHSS) or a ≥2-point drop on the Glasgow coma scale. Poor outcome was defined as a modified Rankin score of 4 to 6 at 3-month follow-up. RESULTS: The rate of HE was higher among those scanned within 3 h (124/304, 40.8%) versus 3 to 24 h post-ICH onset (53/263, 20.2%) (p < 0.001). However, HE was an independent predictor of ND (p < 0.001), poor outcome (p = 0.010), and mortality (p = 0.003) among those scanned within 3 h, as well as those scanned 3-24 h post-ICH (p = 0.043, p = 0.037, and p = 0.004, respectively). Also, in a subset of 180/567 (31.7%) patients presenting with mild symptoms (NIHSS ≤ 5), hematoma growth was an independent predictor of ND (p = 0.026), poor outcome (p = 0.037), and mortality (p = 0.027). CONCLUSION: Despite decreasing rates over time after ICH onset, HE remains an independent predictor of ND, functional outcome, and mortality among those presenting >3 h after onset or with mild symptoms.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

Endovascular Treatment of Acute Ischemic Stroke After Cardiac Interventions in the United States.

Importance: Ischemic stroke is a serious complication of cardiac intervention, including surgery and percutaneous procedures. Endovascular thrombectomy (EVT) is an effective treatment for ischemic stroke and may be particularly important for cardiac intervention patients who often cannot receive intravenous thrombolysis. Objective: To examine trends in EVT for ischemic stroke during hospitalization of patients with cardiac interventions vs those without in the United States. Design, Setting, and Participants: This cohort study involved a retrospective analysis using data for 4888 US hospitals from the 2016-2020 National Inpatient Sample database. Participants included adults (age ≥18 years) with ischemic stroke (per codes from the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification), who were organized into study groups of hospitalized patients with cardiac interventions vs without. Individuals were excluded from the study if they had either procedure prior to admission, EVT prior to cardiac intervention, EVT more than 3 days after admission or cardiac intervention, or endocarditis. Data were analyzed from April 2023 to October 2023. Exposures: Cardiac intervention during admission. Main Outcomes and Measures: The odds of undergoing EVT by cardiac intervention status were calculated using multivariable logistic regression. Adjustments were made for stroke severity in the subgroup of patients who had a National Institutes of Health Stroke Scale (NIHSS) score documented. As a secondary outcome, the odds of discharge home by EVT status after cardiac intervention were modeled. Results: Among 634 407 hospitalizations, the mean (SD) age of the patients was 69.8 (14.1) years, 318 363 patients (50.2%) were male, and 316 044 (49.8%) were female. A total of 12 093 had a cardiac intervention. An NIHSS score was reported in 218 576 admissions, 216 035 (34.7%) without cardiac intervention and 2541 (21.0%) with cardiac intervention (P < .001). EVT was performed in 23 660 patients (3.8%) without cardiac intervention vs 194 (1.6%) of those with cardiac intervention (P < .001). After adjustment for potential confounders, EVT was less likely to be performed in stroke patients with cardiac intervention vs those without (adjusted odds ratio [aOR], 0.27; 95% CI, 0.23-0.31), which remained consistent after adjusting for NIHSS score (aOR, 0.28; 95% CI, 0.22-0.35). Among individuals with a cardiac intervention, receiving EVT was associated with a 2-fold higher chance of discharge home (aOR, 2.21; 95% CI, 1.14-4.29). Conclusions and Relevance: In this study, patients hospitalized with ischemic stroke and cardiac intervention may be less than half as likely to receive EVT as those without cardiac intervention. Given the known benefit of EVT, there is a need to better understand the reasons for lower rates of EVT in this patient population.

Impaired mobility and MRI markers of vascular brain injury: Atherosclerosis Risk in Communities and UK Biobank studies.

Background: Vascular brain injury (VBI) may be an under-recognised contributor to mobility impairment. We examined associations between MRI VBI biomarkers and impaired mobility. Methods: We separately analysed Atherosclerosis Risk in Communities (ARIC) and UK Biobank (UKB) study cohorts. Inclusion criteria were no prevalent clinical stroke, and available brain MRI and balance and gait data. MRI VBI biomarkers were (ARIC: ventricular and white matter hyperintensity (WMH) volumes, non-lacunar and lacunar infarctions, microhaemorrhage; UKB: ventricular, brain and WMH volumes, fractional anisotropy (FA), mean diffusivity (MD), intracellular and isotropic free water volume fractions). Quantitative biomarkers were categorised into tertiles. Mobility impairment outcomes were imbalance and slow walk in ARIC and recent fall and slow walk in UKB. Adjusted multivariable logistic regression analyses were performed. Results: We included 1626 ARIC (mean age 76.2 years; 23.4% imbalance, 25.0% slow walk) and 40 098 UKB (mean age 55 years; 15.8% falls, 2.8% slow walk) participants. In ARIC, imbalance associated with four of five VBI measures (all p values<0.05), most strongly with WMH (adjusted OR, aOR 1.64; 95% CI 1.18 to 2.29). Slow walk associated with four of five VBI measures, most strongly with WMH (aOR 2.32; 95% CI 1.66 to 3.24). In UKB, falls associated with all VBI measures except WMH, most strongly with FA (aOR 1.16; 95% CI 1.08 to 1.24). Slow walking associated with WMH, FA and MD, most strongly with FA (aOR 1.57; 95% CI 1.32 to 1.87). Conclusions: VBI is associated with mobility impairment in community-dwelling, clinically stroke-free cohorts. Consequences of VBI may extend beyond clinically apparent stroke to include mobility.

COVID-19 Infection Is Associated With Poor Outcomes in Patients With Intracerebral Hemorrhage.

BACKGROUND: Patients with ischemic stroke and concomitant COVID-19 infection have worse outcomes than those without this infection, but the impact of COVID-19 on hemorrhagic stroke remains unclear. We aimed to assess if COVID-19 worsens outcomes in intracerebral hemorrhage (ICH). METHODS AND RESULTS: We conducted an observational study of ICH outcomes using Get With The Guidelines Stroke data. We compared patients with ICH who were COVID-19 positive and negative during the pandemic (March 2020-February 2021) and prepandemic (March 2019-February 2020). Main outcomes were poor functional outcome (defined as a modified Rankin scale score of 4 to 6 at discharge), mortality, and discharge to a skilled nursing facility or hospice. The first stage included 60 091 patients with ICH who were COVID-19 negative and 1326 COVID-19 positive. In multivariable analyses, patients with ICH with versus without COVID-19 infection had 68% higher odds of poor outcome (odds ratio [OR], 1.68 [95% CI, 1.41-2.01]), 51% higher odds of mortality (OR, 1.51 [95% CI, 1.33-1.71]), and 66% higher odds of being discharged to a skilled nursing facility/hospice (OR, 1.66 [95% CI, 1.43-1.93]). The second stage included 62 743 prepandemic and 64 681 intrapandemic cases with ICH. In multivariable analyses, patients with ICH admitted during versus before the COVID-19 pandemic had 10% higher odds of poor outcomes (OR, 1.10 [95% CI, 1.07-1.14]), 5% higher mortality (OR, 1.05 [95% CI, 1.02-1.08]), and no significant difference in the risk of being discharged to a skilled nursing facility/hospice (OR, 0.93 [95% CI, 0.90-0.95]). CONCLUSIONS: The pathophysiology of the COVID-19 infection and changes in health care delivery during the pandemic played a role in worsening outcomes in the patient population with ICH.