An Approach to Successful Development of Clinician-Scientists in Neurology: The NINDS R25 Experience.

OBJECTIVE: Training clinician-scientists is a primary objective of many academic neurology departments, as these individuals are uniquely positioned to perform insightful clinical or laboratory-based research informed both by clinical knowledge and their own experiences caring for patients. Despite its importance, training clinician-scientists has perhaps never been so challenging. The National Institute of Neurologic Disorders and Stroke (NINDS) R25 program was designed in an attempt to support these individuals, decrease the time needed to obtain National Institutes of Health K awards, and to help educate a cohort of trainees preparing for a career in academic neurology. We endeavored to describe the structure and features of the program while examining its outcomes. METHODS: R25 outcome data from 2009 to 2024 were reviewed. Statistical comparisons were made using 2-sided Mann-Whitney U testing. RESULTS: A total of 67% of adult neurologists who received an R25 had a successful application for a National Institutes of Health K award compared with 45% of adult neurologists who had not received R25 support (p < 0.0001). Among child neurologists, 73% who applied went on to receive K funding after R25 support, compared with 45% who had not been part of the R25 program (p < 0.001). The average time between completion of residency and obtaining a K award for R25 participants was decreased by 26 months among those with an MD/PhD degree, and 32 months for those with an MD degree compared with non-R25 individuals. INTERPRETATION: The R25 program has been successful in achieving its training goals, but stands as only one component of support for aspiring clinician-scientists. Investments and commitments made by academic neurology departments are key to supporting this success. ANN NEUROL 2024.

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.

Biological and Procedural Predictors of Outcome in the Stroke Preclinical Assessment Network (SPAN) Trial.

BACKGROUND: The SPAN trial (Stroke Preclinical Assessment Network) is the largest preclinical study testing acute stroke interventions in experimental focal cerebral ischemia using endovascular filament middle cerebral artery occlusion (MCAo). Besides testing interventions against controls, the prospective design captured numerous biological and procedural variables, highlighting the enormous heterogeneity introduced by the multicenter structure that might influence stroke outcomes. Here, we leveraged the unprecedented sample size achieved by the SPAN trial and the prospective design to identify the biological and procedural variables that affect experimental stroke outcomes in transient endovascular filament MCAo. METHODS: The study cohort included all mice enrolled and randomized in the SPAN trial (N=1789). Mice were subjected to 60-minute MCAo and followed for a month. Thirteen biological and procedural independent variables and 4 functional (weight loss and 4-point neuroscore on days 1 and 2, corner test on days 7 and 28, and mortality) and 3 tissue (day 2, magnetic resonance imaging infarct volumes and swelling; day 30, magnetic resonance imaging tissue loss) outcome variables were prospectively captured. Multivariable regression with stepwise elimination was used to identify the predictors and their effect sizes. RESULTS: Older age, active circadian stage at MCAo, and thinner and longer filament silicone tips predicted higher mortality. Older age, larger body weight, longer anesthesia duration, and longer filament tips predicted worse neuroscores, while high-fat diet and blood flow monitoring predicted milder neuroscores. Older age and a high-fat diet predicted worse corner test performance. While shorter filament tips predicted more ipsiversive turning, longer filament tips appeared to predict contraversive turning. Age, sex, and weight interacted when predicting the infarct volume. Older age was associated with smaller infarcts on day 2 magnetic resonance imaging, especially in animals with larger body weights; this association was most conspicuous in females. High-fat diet also predicted smaller infarcts. In contrast, the use of cerebral blood flow monitoring and more severe cerebral blood flow drop during MCAo, longer anesthesia, and longer filament tips all predicted larger infarcts. Bivariate analyses among the dependent variables highlighted a disconnect between tissue and functional outcomes. CONCLUSIONS: Our analyses identified variables affecting endovascular filament MCAo outcome, an experimental stroke model used worldwide. Multiple regression refuted some commonly reported predictors and revealed previously unrecognized associations. Given the multicenter prospective design that represents a sampling of real-world conditions, the degree of heterogeneity mimicking clinical trials, the large number of predictors adjusted for in the multivariable model, and the large sample size, we think this is the most definitive analysis of the predictors of preclinical stroke outcome to date. Future multicenter experimental stroke trials should standardize or at least ensure a balanced representation of the biological and procedural variables identified herein as potential confounders.

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.

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.

VEGF-C prophylaxis favors lymphatic drainage and modulates neuroinflammation in a stroke model.

Meningeal lymphatic vessels (MLVs) promote tissue clearance and immune surveillance in the central nervous system (CNS). Vascular endothelial growth factor-C (VEGF-C) regulates MLV development and maintenance and has therapeutic potential for treating neurological disorders. Herein, we investigated the effects of VEGF-C overexpression on brain fluid drainage and ischemic stroke outcomes in mice. Intracerebrospinal administration of an adeno-associated virus expressing mouse full-length VEGF-C (AAV-mVEGF-C) increased CSF drainage to the deep cervical lymph nodes (dCLNs) by enhancing lymphatic growth and upregulated neuroprotective signaling pathways identified by single nuclei RNA sequencing of brain cells. In a mouse model of ischemic stroke, AAV-mVEGF-C pretreatment reduced stroke injury and ameliorated motor performances in the subacute stage, associated with mitigated microglia-mediated inflammation and increased BDNF signaling in brain cells. Neuroprotective effects of VEGF-C were lost upon cauterization of the dCLN afferent lymphatics and not mimicked by acute post-stroke VEGF-C injection. We conclude that VEGF-C prophylaxis promotes multiple vascular, immune, and neural responses that culminate in a protection against neurological damage in acute ischemic stroke.

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.

Association of Neighborhood-Level Socioeconomic Factors With Delay to Hospital Arrival in Patients With Acute Stroke.

BACKGROUND AND OBJECTIVES: Delivery of acute ischemic stroke (AIS) therapies is contingent on the duration from last known well (LKW) to emergency department arrival time (EDAT). One reason for treatment ineligibility is delay in presentation to the hospital. We evaluate patient and neighborhood characteristics associated with time from LKW to EDAT. METHODS: This was a retrospective observational study of patients presenting to the Yale New Haven Hospital in the AIS code pathway from 2010 to 2020. Patients presenting within 4.5 hours from LKW who were recorded in the institutional Get With the Guidelines Stroke registry were classified as early while those presenting beyond 4.5 hours were designated as late. Temporal trends in late presentation were explored by univariate logistic regression. Using variables significant in univariate analysis at p < 0.05, we developed a mixed-effect logistic regression model to estimate the probability of late presentation as a function of patient-level and neighborhood (ZIP)-level characteristics (area deprivation index [ADI] derived from the Health Resources and Services Administration), adjusted for calendar year and geographic distance from the centroid of the ZIP code to the hospital. RESULTS: A total of 2,643 patients with AIS from 2010 to 2020 were included (63.4% presented late and 36.6% presented early). The frequency of late presentation increased significantly from 68% in 2010 to 71% in 2020 (p = 0.002) and only among non-White patients. Patients presenting late were more likely to be non-White (37.1% vs 26.9%, p < 0.0001), arrive by means other than emergency medical services (EMS) (32.7% vs 16.1%, p < 0.0001), have an NIHSS <6 (68.7% vs 55.2%, p < 0.0001), and present from a neighborhood with a higher ADI category (p = 0.0001) that was nearer to the hospital (median 5.8 vs 7.7 miles, p = 0.0032). In the mixed model, the ADI by units of 10 (odds ratio [OR] 1.022, 95% confidence interval [CI] 1.020-1.024), non-White race (OR 1.083, 95% CI 1.039-1.127), arrival by means other than EMS (OR 1.193, 95% CI 1.145-1.124), and an NIHSS <6 (OR 1.085, 95% CI 1.041-1.129) were associated with late presentation. DISCUSSION: In addition to patient-level factors, socioeconomic deprivation of neighborhood of residence contributes to delays in hospital presentation for AIS. These findings may provide opportunities for targeted interventions to improve presentation times in at-risk communities.

Clinical Trial Protocol for BEACH: A Phase 2a Study of MW189 in Patients with Acute Nontraumatic Intracerebral Hemorrhage.

Patients with acute spontaneous intracerebral hemorrhage (ICH) develop secondary neuroinflammation and cerebral edema that can further damage the brain and lead to increased risk of neurologic complications. Preclinical studies in animal models of acute brain injury have shown that a novel small-molecule drug candidate, MW01-6-189WH (MW189), decreases neuroinflammation and cerebral edema and improves functional outcomes. MW189 was also safe and well tolerated in phase 1 studies in healthy adults. The proof-of-concept phase 2a Biomarker and Edema Attenuation in IntraCerebral Hemorrhage (BEACH) clinical trial is a first-in-patient, multicenter, randomized, double-blind, placebo-controlled trial. It is designed to determine the safety and tolerability of MW189 in patients with acute ICH, identify trends in potential mitigation of neuroinflammation and cerebral edema, and assess effects on functional outcomes. A total of 120 participants with nontraumatic ICH will be randomly assigned 1:1 to receive intravenous MW189 (0.25 mg/kg) or placebo (saline) within 24 h of symptom onset and every 12 h for up to 5 days or until hospital discharge. The 120-participant sample size (60 per group) will allow testing of the null hypothesis of noninferiority with a tolerance limit of 12% and assuming a "worst-case" safety assumption of 10% rate of death in each arm with 10% significance and 80% power. The primary outcome is all-cause mortality at 7 days post randomization between treatment arms. Secondary end points include all-cause mortality at 30 days, perihematomal edema volume after symptom onset, adverse events, vital signs, pharmacokinetics of MW189, and inflammatory cytokine concentrations in plasma (and cerebrospinal fluid if available). Other exploratory end points are functional outcomes collected on days 30, 90, and 180. BEACH will provide important information about the utility of targeting neuroinflammation in ICH and will inform the design of future larger trials of acute central nervous system injury.