Characterization of CSF inflammatory markers after hemorrhagic stroke and their relationship to disease severity.

Background: The inflammatory response within the central nervous system is a key driver of secondary brain injury after hemorrhagic stroke, both in patients with intracerebral hemorrhage (ICH) and aneurysmal subarachnoid hemorrhage (aSAH). In this study, we aimed to characterize inflammatory molecules in the blood and cerebrospinal fluid (CSF) of patients within 72 hours of hemorrhage to understand how such molecules vary across disease types and disease severity. Methods: Biological samples were collected from patients admitted to a single-center Neurosciences Intensive Care Unit with a diagnosis of ICH or aSAH between 2014 and 2022. Control CSF samples were collected from patients undergoing CSF diversion for normal pressure hydrocephalus. A panel of immune molecules in the plasma and CSF samples was analyzed using Cytometric Bead Array assays. Clinical variables, including demographics, disease severity, and intensive care unit length of stay were collected. Results: Plasma and/or CSF samples were collected from 260 patients (188 ICH patients, 54 aSAH patients, 18 controls). C-C motif chemokine ligand-2 (CCL2), interleukin-6 (IL-6), granulocyte-colony stimulating factor (G-CSF), interleukin-8 (IL-8), and vascular endothelial growth factor (VEGF), were detectable in the CSF within the first 3 days after hemorrhage, and all were elevated compared to plasma. Compared with controls, CCL2, IL-6, IL-8, G-CSF, and VEGF were elevated in the CSF of both ICH and aSAH patients (p<0.01 for all comparisons). VEGF was increased in ICH patients compared to aSAH patients (p<0.01). CCL2, G-CSF, and VEGF in the CSF were associated with more severe disease in aSAH patients only. Conclusions: Within 3 days of hemorrhagic stroke, proinflammatory molecules can be detected in the CSF at higher concentrations than in the plasma. Early concentrations of some pro-inflammatory molecules may be associated with markers of disease severity.

A multi-laboratory preclinical trial in rodents to assess treatment candidates for acute ischemic stroke.

Human diseases may be modeled in animals to allow preclinical assessment of putative new clinical interventions. Recent, highly publicized failures of large clinical trials called into question the rigor, design, and value of preclinical assessment. We established the Stroke Preclinical Assessment Network (SPAN) to design and implement a randomized, controlled, blinded, multi-laboratory trial for the rigorous assessment of candidate stroke treatments combined with intravascular thrombectomy. Efficacy and futility boundaries in a multi-arm multi-stage statistical design aimed to exclude from further study highly effective or futile interventions after each of four sequential stages. Six independent research laboratories performed a standard focal cerebral ischemic insult in five animal models that included equal numbers of males and females: young mice, young rats, aging mice, mice with diet-induced obesity, and spontaneously hypertensive rats. The laboratories adhered to a common protocol and efficiently enrolled 2615 animals with full data completion and comprehensive animal tracking. SPAN successfully implemented treatment masking, randomization, prerandomization inclusion and exclusion criteria, and blinded assessment of outcomes. The SPAN design and infrastructure provide an effective approach that could be used in similar preclinical, multi-laboratory studies in other disease areas and should help improve reproducibility in translational science.

Racial/Ethnic Variation in White Matter Hyperintensity Progression in the ACCORDION MIND Study.

BACKGROUND: White matter hyperintensities (WMHs) are linked to cognitive decline and stroke. We investigate the impact of race on WMH progression in the Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORDION MIND) trial. MATERIALS AND METHODS: The primary outcome is WMH progression in milliliters, evaluated by fitting linear regression to WMH volume on the month 80 magnetic resonance imaging (MRI) and including the WMH volume on the baseline MRI. The primary predictor is patient race, with the exclusion of patients defined as "other" race. We also derived predicted probabilities of our outcome for systolic blood pressure (SBP) levels. RESULTS: We included 276 patients who completed the baseline and month 80 MRI, of which 207 (75%) were White, 48 (17%) Black, and 21 (8%) Hispanic. During follow-up, the mean number of SBP, low-density lipoprotein (LDL), and A1c measurements per patient was 21, 8, and 15. The median (IQR) WMH progression was 1.5 mL (0.5 to 3.9) for Black patients, 1.0 mL (0.4 to 4.0) for Hispanics, and 1.3 mL (0.5 to 2.7) for Whites (Kruskal-Wallis test, P =0.59). In the multivariate regression model, Black, compared with White, patients had significantly more WMH progression (ß Coefficient 1.26, 95% confidence interval 0.45 to 2.06, P =0.002). Hispanic, compared with White, patients neither have significantly different WMH progression ( P =0.392), nor was there a difference when comparing Hispanic to Black patients ( P =0.162). The predicted WMH progression was significantly higher for Black compared with White patients across a mean SBP of 117 to 139 mm Hg. CONCLUSIONS: Black diabetic patients in Action to Control Cardiovascular Risk in Diabetes Memory in Diabetes (ACCORDION MIND) have a higher risk of WMH progression than White patients across a normal range of SBP.

Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.

Importance: Clinical text reports from head computed tomography (CT) represent rich, incompletely utilized information regarding acute brain injuries and neurologic outcomes. CT reports are unstructured; thus, extracting information at scale requires automated natural language processing (NLP). However, designing new NLP algorithms for each individual injury category is an unwieldy proposition. An NLP tool that summarizes all injuries in head CT reports would facilitate exploration of large data sets for clinical significance of neuroradiological findings. Objective: To automatically extract acute brain pathological data and their features from head CT reports. Design, Setting, and Participants: This diagnostic study developed a 2-part named entity recognition (NER) NLP model to extract and summarize data on acute brain injuries from head CT reports. The model, termed BrainNERD, extracts and summarizes detailed brain injury information for research applications. Model development included building and comparing 2 NER models using a custom dictionary of terms, including lesion type, location, size, and age, then designing a rule-based decoder using NER outputs to evaluate for the presence or absence of injury subtypes. BrainNERD was evaluated against independent test data sets of manually classified reports, including 2 external validation sets. The model was trained on head CT reports from 1152 patients generated by neuroradiologists at the Yale Acute Brain Injury Biorepository. External validation was conducted using reports from 2 outside institutions. Analyses were conducted from May 2020 to December 2021. Main Outcomes and Measures: Performance of the BrainNERD model was evaluated using precision, recall, and F1 scores based on manually labeled independent test data sets. Results: A total of 1152 patients (mean [SD] age, 67.6 [16.1] years; 586 [52%] men), were included in the training set. NER training using transformer architecture and bidirectional encoder representations from transformers was significantly faster than spaCy. For all metrics, the 10-fold cross-validation performance was 93% to 99%. The final test performance metrics for the NER test data set were 98.82% (95% CI, 98.37%-98.93%) for precision, 98.81% (95% CI, 98.46%-99.06%) for recall, and 98.81% (95% CI, 98.40%-98.94%) for the F score. The expert review comparison metrics were 99.06% (95% CI, 97.89%-99.13%) for precision, 98.10% (95% CI, 97.93%-98.77%) for recall, and 98.57% (95% CI, 97.78%-99.10%) for the F score. The decoder test set metrics were 96.06% (95% CI, 95.01%-97.16%) for precision, 96.42% (95% CI, 94.50%-97.87%) for recall, and 96.18% (95% CI, 95.151%-97.16%) for the F score. Performance in external institution report validation including 1053 head CR reports was greater than 96%. Conclusions and Relevance: These findings suggest that the BrainNERD model accurately extracted acute brain injury terms and their properties from head CT text reports. This freely available new tool could advance clinical research by integrating information in easily gathered head CT reports to expand knowledge of acute brain injury radiographic phenotypes.

The Stroke Preclinical Assessment Network: Rationale, Design, Feasibility, and Stage 1 Results.

Cerebral ischemia and reperfusion initiate cellular events in brain that lead to neurological disability. Investigating these cellular events provides ample targets for developing new treatments. Despite considerable work, no such therapy has translated into successful stroke treatment. Among other issues-such as incomplete mechanistic knowledge and faulty clinical trial design-a key contributor to prior translational failures may be insufficient scientific rigor during preclinical assessment: nonblinded outcome assessment; missing randomization; inappropriate sample sizes; and preclinical assessments in young male animals that ignore relevant biological variables, such as age, sex, and relevant comorbid diseases. Promising results are rarely replicated in multiple laboratories. We sought to address some of these issues with rigorous assessment of candidate treatments across 6 independent research laboratories. The Stroke Preclinical Assessment Network (SPAN) implements state-of-the-art experimental design to test the hypothesis that rigorous preclinical assessment can successfully reduce or eliminate common sources of bias in choosing treatments for evaluation in clinical studies. SPAN is a randomized, placebo-controlled, blinded, multilaboratory trial using a multi-arm multi-stage protocol to select one or more putative stroke treatments with an implied high likelihood of success in human clinical stroke trials. The first stage of SPAN implemented procedural standardization and experimental rigor. All participating research laboratories performed middle cerebral artery occlusion surgery adhering to a common protocol and rapidly enrolled 913 mice in the first of 4 planned stages with excellent protocol adherence, remarkable data completion and low rates of subject loss. SPAN stage 1 successfully implemented treatment masking, randomization, prerandomization inclusion/exclusion criteria, and blinded assessment to exclude bias. Our data suggest that a large, multilaboratory, preclinical assessment effort to reduce known sources of bias is feasible and practical. Subsequent SPAN stages will evaluate candidate treatments for potential success in future stroke clinical trials using aged animals and animals with comorbid conditions.

Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state.

A classic view of the striatum holds that activity in direct and indirect pathways oppositely modulates motor output. Whether this involves direct control of movement, or reflects a cognitive process underlying movement, remains unresolved. Here we find that strong, opponent control of behavior by the two pathways of the dorsomedial striatum depends on the cognitive requirements of a task. Furthermore, a latent state model (a hidden Markov model with generalized linear model observations) reveals that-even within a single task-the contribution of the two pathways to behavior is state dependent. Specifically, the two pathways have large contributions in one of two states associated with a strategy of evidence accumulation, compared to a state associated with a strategy of repeating previous choices. Thus, both the demands imposed by a task, as well as the internal state of mice when performing a task, determine whether dorsomedial striatum pathways provide strong and opponent control of behavior.

Hospital Revisits for Post-Ischemic Stroke Epilepsy after Acute Stroke Interventions.

OBJECTIVES: Improvements in acute stroke care have led to an increase in ischemic stroke survivors, who are at risk for development of post-ischemic stroke epilepsy (PISE). The impact of therapies such as thrombectomy and thrombolysis on risk of hospital revisits for PISE is unclear. We utilized administrative data to investigate the association between stroke treatment and PISE-related visits. MATERIALS AND METHODS: Using claims data from California, New York, and Florida, we performed a retrospective analysis of adult survivors of acute ischemic strokes. Patients with history of epilepsy, trauma, infections, or tumors were excluded. Included patients were followed for a primary outcome of revisits for seizures or epilepsy. Cox proportional hazards regression was used to identify covariates associated with PISE. RESULTS: In 595,545 included patients (median age 74 [IQR 21], 52% female), the 6-year cumulative rate of PISE-related revisit was 2.20% (95% CI 2.16-2.24). In multivariable models adjusting for demographics, comorbidities, and indicators of stroke severity, IV-tPA (HR 1.42, 95% CI 1.31-1.54, p<0.001) but not MT (HR 1.62, 95% CI 0.90-1.50, p=0.2) was associated with PISE-related revisit. Patients who underwent decompressive craniectomy experienced a 2-fold increase in odds for returning with PISE (HR 2.35, 95% CI 1.69-3.26, p<0.001). In-hospital seizures (HR 4.06, 95% CI 3.76-4.39, p<0.001) also elevated risk for PISE. SIGNIFICANCE: We demonstrate that ischemic stroke survivors who received IV-tPA, underwent decompressive craniectomy, or experienced acute seizures were at increased risk PISE-related revisit. Close attention should be paid to these patients with increased potential for long-term development of and re-hospitalization for PISE.

The Utility of Quantitative EEG in Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

SUMMARY: In this review, we discuss the utility of quantitative EEG parameters for the detection of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage in the context of the complex pathophysiology of DCI and the limitations of current diagnostic methods. Because of the multifactorial pathophysiology of DCI, methodologies solely assessing blood vessel narrowing (vasospasm) are insufficient to detect all DCI. Quantitative EEG has facilitated the exploration of EEG as a diagnostic modality of DCI. Multiple quantitative EEG parameters such as alpha power, relative alpha variability, and alpha/delta ratio show reliable detection of DCI in multiple studies. Recent studies on epileptiform abnormalities suggest that their potential for the detection of DCI. Quantitative EEG is a promising, continuous, noninvasive, monitoring modality of DCI implementable in daily practice. Future work should validate these parameters in larger populations, facilitated by the development of automated detection algorithms and multimodal data integration.

Self-Reported Race as a Social Determinant of Stroke Risk in Observational Versus Clinical Trial Datasets.

OBJECTIVES: Self-reported Black (SRB) Americans are approximately twice as likely to have a stroke as self-reported White (SRW) Americans. While social determinants of health and vascular risk factors account for some of the disparity, half the increased risk remains unexplained and may be related to unmeasured real-world factors of the racialized experience. MATERIALS: and Methods In this cohort study, we compared SRB and SRW participants in the Systolic Blood Pressure Intervention Trial (SPRINT) to the same groups in the observational Atherosclerosis Risk in Communities (ARIC) study to evaluate if clinical trial participation mitigates disparities in stroke risk. We set the ARIC baseline at visit 4 and excluded participants with prior stroke to create an ARIC cohort similar in age to SPRINT participants. The study outcome was incident primary stroke. We report hazard ratios from Cox models and inverse-probability weighted Cox models with propensity score matching on participant age, sex, diabetes, atrial fibrillation, and smoking. RESULTS: We included 10,094 patients from ARIC and 8,869 from SPRINT, of which 26.1% were SRB. The risk of stroke between SRW participants in SPRINT versus ARIC was not significantly different (IPW-Weighted HR 0.78 [0.52-1.19]). SRB ARIC participants were twice as likely to have a stroke as SRW ARIC participants (IPW-Weighted HR = 1.96 [1.41-2.71]). However, SRB SPRINT participants did not have higher stroke risk compared to SRW SPRINT or ARIC participants (IPW-Weighted HR 0.99 [0.68--1.77] and 0.95 [.57-1.59], respectively). SRB SPRINT participants in the intensive BP control group had a lower risk of stroke compared to SRB ARIC participants (IPW-Weighted HR = 0.39 [0.20-0.75]). CONCLUSIONS: SRB race, compared to SRW race, is associated with an increase in primary stroke risk in the ARIC study but not in the SPRINT trial. The absence of the racial disparity in stroke incidence in SPRINT indicates that aspects of the disparity are modifiable. Population-based interventions that test this hypothesis deserve further attention.

Prior Stroke and Age Predict Acute Ischemic Stroke Among Hospitalized COVID-19 Patients: A Derivation and Validation Study.

Objectives: Our objective was to identify characteristics associated with having an acute ischemic stroke (AIS) among hospitalized COVID-19 patients and the subset of these patients with a neurologic symptom. Materials and Methods: Our derivation cohort consisted of COVID-19 patients admitted to Yale-New Haven Health between January 3, 2020 and August 28, 2020 with and without AIS. We also studied a sub-cohort of hospitalized COVID-19 patients demonstrating a neurologic symptom with and without an AIS. Demographic, clinical, and laboratory results were compared between AIS and non-AIS patients in the full COVID-19 cohort and in the sub-cohort of COVID-19 patients with a neurologic symptom. Multivariable logistic regression models were built to predict ischemic stroke risk in these two COVID-19 cohorts. These 2 models were externally validated in COVID-19 patients hospitalized at a major health system in New York. We then compared the distribution of the resulting predictors in a non-COVID ischemic stroke control cohort. Results: A total of 1,827 patients were included in the derivation cohort (AIS N = 44; no AIS N = 1,783). Among all hospitalized COVID-19 patients, history of prior stroke and platelet count ≥ 200 × 1,000/µL at hospital presentation were independent predictors of AIS (derivation AUC 0.89, validation AUC 0.82), irrespective of COVID-19 severity. Among hospitalized COVID-19 patients with a neurologic symptom (N = 827), the risk of AIS was significantly higher among patients with a history of prior stroke and age <60 (derivation AUC 0.83, validation AUC 0.81). Notably, in a non-COVID ischemic stroke control cohort (N = 168), AIS patients were significantly older and less likely to have had a prior stroke, demonstrating the uniqueness of AIS patients with COVID-19. Conclusions: Hospitalized COVID-19 patients who demonstrate a neurologic symptom and have either a history of prior stroke or are of younger age are at higher risk of ischemic stroke.