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Background We hypothesized that stroke outcome is related to multiple baseline hydration‐related factors including volume contracted state (VCS) and diuretic use. Methods and Results We analyzed a prospective cohort of subjects with ischemic stroke Conclusions A VCS at the time of hospitalization was associated with more severe stroke and odds of death but not associated with worse functional outcome when accounting for relevant characteristics. Diuretic use and low serum potassium at the time of stroke onset were associated with worse outcome and may be worthy of further investigation.

Delayed cerebral ischemia (DCI) continues to be a sequela of aneurysmal subarachnoid hemorrhage (aSAH) that carries significant morbidity and mortality. Aside from nimodipine, no therapeutic agents are available to reduce the incidence of DCI. Pathophysiologic mechanisms contributing to DCI are poorly understood, but accumulating evidence over the years implicates several factors. Those have included microvessel vasoconstriction, microthrombosis, oxidative tissue damage, and cortical spreading depolarization as well as large vessel vasospasm. Common to these processes is red blood cell leakage into the cerebrospinal fluids (CSF) and subsequent lysis which releases hemoglobin, a central instigator in these events. This has led to the hypothesis that early blood removal may improve clinical outcome and reduce DCI. This paper will provide a narrative review of the evidence of hemoglobin as an instigator of DCI. It will also elaborate on available human data that discuss blood clearance and CSF drainage as a treatment of DCI. Finally, we will address a recent novel device that is currently being tested, the Neurapheresis CSF Management System™. This is an automated dual-lumen lumbar drainage system that has an option to filter CSF and return it to the patient.

ObjectiveThe widespread adoption of endovascular therapy (EVT) for emergent large vessel occlusion has led to increased nationwide demand for neurointerventionalists, heightened interest among neurology residents to pursue neurointervention as a career, and increased importance of neurointervention exposure for all neurologists who care for patients with acute ischemic stroke. Exposure to neurointervention and its career path are not well-defined for neurology trainees.MethodsThe Society for Vascular and Interventional Neurology (SVIN) Education Committee conducted a multicenter electronic survey directed towards neurology residents and vascular neurology (VN), neurocritical care (NCC), and neurointervention fellows in June 2018. A total of 250 programs were invited to participate; 76 trainees completed the survey.ResultsRespondents self-identified as 22% postgraduate year (PGY)2, 40% PGY3/4, 30% VN fellows, and 8% neurointervention or NCC fellows. Eighty-seven percent of trainees had more than 2 months exposure to VN during residency, 41% to NCC, and only 3% to neurointervention. Sixty-eight percent of respondents had no exposure to neurointervention during residency. Whereas 72% believed that a background in neurology was good preparation for neurointervention, only 41% agreed that fellowship training pathway in neurointervention is well-structured for neurology residents when compared to other subspecialties.ConclusionIn this survey, respondents identified lack of exposure to neurointervention and a well-defined training pathway as obstacles towards pursuing neurointervention as a career. These obstacles must be addressed for the continued development of neurointervention as a subspecialty of neurology.

Background Inter-observer variability in stroke aetiological classification may have an effect on trial power and estimation of treatment effect. We modelled the effect of misclassification on required sample size in a hypothetical cardioembolic (CE) stroke trial. Methods We performed a systematic review to quantify the reliability (inter-observer variability) of various stroke aetiological classification systems. We then modelled the effect of this misclassification in a hypothetical trial of anticoagulant in CE stroke contaminated by patients with non-cardioembolic (non-CE) stroke aetiology. Rates of misclassification were based on the summary reliability estimates from our systematic review. We randomly sampled data from previous acute trials in CE and non-CE participants, using the Virtual International Stroke Trials Archive. We used bootstrapping to model the effect of varying misclassification rates on sample size required to detect a between-group treatment effect across 5000 permutations. We described outcomes in terms of survival and stroke recurrence censored at 90 days. Results From 4655 titles, we found 14 articles describing three stroke classification systems. The inter-observer reliability of the classification systems varied from ‘fair’ to ‘very good’ and suggested misclassification rates of 5% and 20% for our modelling. The hypothetical trial, with 80% power and alpha 0.05, was able to show a difference in survival between anticoagulant and antiplatelet in CE with a sample size of 198 in both trial arms. Contamination of both arms with 5% misclassified participants inflated the required sample size to 237 and with 20% misclassification inflated the required sample size to 352, for equivalent trial power. For an outcome of stroke recurrence using the same data, base-case estimated sample size for 80% power and alpha 0.05 was n = 502 in each arm, increasing to 605 at 5% contamination and 973 at 20% contamination. Conclusions Stroke aetiological classification systems suffer from inter-observer variability, and the resulting misclassification may limit trial power. Trial registration Protocol available at reviewregistry540. Electronic supplementary material The online version of this article (10.1186/s13063-019-3222-x) contains supplementary material, which is available to authorized users.

Background and aim The availability and access of hospital administrative data [coding for Charlson comorbidity index (CCI)] in large data form has resulted in a surge of interest in using this information to predict mortality from stroke. The aims of this study were to determine the minimum clinical data set to be included in models for predicting disability after ischemic stroke adjusting for CCI and clinical variables and to evaluate the impact of CCI on prediction of outcome. Method We leverage anonymized clinical trial data in the Virtual International Stroke Trials Archive. This repository contains prospective data on stroke severity and outcome. The inclusion criteria were patients with available stroke severity score such as National Institutes of Health Stroke Scale (NIHSS), imaging data, and outcome disability score such as 90-day Rankin Scale. We calculate CCI based on comorbidity data in this data set. For logistic regression, we used these calibration statistics: Nagelkerke generalised R2 and Brier score; and for discrimination we used: area under the receiver operating characteristics curve (AUC) and integrated discrimination improvement (IDI). The IDI was used to evaluate improvement in disability prediction above baseline model containing age, sex, and CCI. Results The clinical data among 5,206 patients (55% males) were as follows: mean age 69 ± 13 years, CCI 4.2 ± 0.8, and median NIHSS of 12 (IQR 8, 17) on admission and 9 (IQR 5, 15) at 24 h. In Model 2, adding admission NIHSS to the baseline model improved AUC from 0.67 (95% CI 0.65–0.68) to 0.79 (95% CI 0.78–0.81). In Model 3, adding 24-h NIHSS to the baseline model resulted in substantial improvement in AUC to 0.90 (95% CI 0.89–0.91) and increased IDI by 0.23 (95% CI 0.22–0.24). Adding the variable recombinant tissue plasminogen activator did not result in a further change in AUC or IDI to this regression model. In Model 3, the variable NIHSS at 24 h explains 87.3% of the variance of Model 3, follow by age (8.5%), comorbidity (3.7%), and male sex (0.5%). Conclusion Our results suggest that prediction of disability after ischemic stroke should at least include 24-h NIHSS and age. The variable CCI is less important for prediction of disability in this data set.

Introduction: Hematoma enlargement (HE) in intracerebral hemorrhage (ICH) possibly occurs soon after onset but is not captured due to delays in imaging. The Mobile Stroke Unit (MSU) can identify ICH patients in the prehospital setting and can increase our understanding of ICH patients in the first hour of onset. Methods: This was a prospective, observational study to examine patients within 4 hours of symptom onset and early HE in ICH due to hypertension and/or coagulopathies. A baseline head CT (HCT) was performed on the MSU, followed by a 1-hour HCT at the destination hospital. Patients during MSU control weeks were included but not required to have a 1-hour HCT. Blood pressure was treated to a systolic goal of 6cc or >30% between scans. Patients were categorized by time to first HCT (≤61min, 62-120min, and 120-270min). Overall HE for each group was calculated using the scan nearest to 24 hours. HE on the 1-hour HCT was calculated for the ≤61min group and for the all patients collectively. The additional HE between the 1-hour HCT and scan nearest to 24 hours was also captured. Results: Twenty-three patients (19 MSU) were included. Four of 8 (50%) patients in the ≤61min group, 3/7 (42.9%) in the 62-120min group, and 3/8 (37.5%) in the 120-270min group had HE in the first 24hrs, p=1.0 (Figure 1). Among the patients who had a 1-hour HCT, the incidence of HE over the first hour in the ≤61min group was 0.4 (2/5) and 0.36 (4/11) for all 3 groups. Additional HE beyond the 1-hour HCT occurred in only 1/9 patients (no follow up scan in 2 patients). Conclusion: There was a high incidence of HE 1 to 2 hours after onset in ICH patients. This period may be an optimal window for treatment. Limitations include a small sample size and confounding due to early intervention in the MSU group. A larger study is currently ongoing.

Background: A Mobile Stroke Unit (MSU) delivers Emergency Department (ED) care in the pre-hospital setting. We describe establishing the first MSU in the U.S. Methods: A full time Medical Director and Project Manager took ownership of the project. A funding plan was developed to build,staff and supply the unit for 3 years. An ambulance company donated a standard ambulance. MSU build-out began after purchase of the CT scanner. A consortium of stakeholders was formed including leaders of the stroke programs and EDs of the city’s 3 Comprehensive Stroke Centers (CSCs) and Houston Emergency Medical Services (EMS). Policies and procedures were developed to obtain both state and city health department licensing and inspection, and an accountability system to maintain the unit to standards. Radiation safety certification was obtained from the state health department. Radiation safety experts inspected the vehicle,obtained rad. measurements and designed safety procedures,confirming minimal risk. Insurance on the vehicle and all equipment was obtained. Staffing was hired,and a secure location to park the MSU with electrical hook-up and an office were located. An EMS education program was carried out and communication pathway developed enabling immediate MSU dispatch within a 5 mile radius following a 911 call, as well as for first-responders to activate the MSU. A study protocol was written to evaluate logistics,cost-effectiveness,telemedicine (TM) vs. on-scene decisions and patient outcomes. A HIPPA-compliant technology grid enabled viewing of CT images by the TM team and receiving hospital radiologist. Results: The MSU effort began in March 2013 and the MSU received Feb 4, 2014. The Houston MSU is a standard 12’ ambulance staffed by a neurologist and registered nurse with stroke expertise, CT tech, and paramedic, with TM connectivity, responding to acute stroke dispatches daily from 8a-6p. All MSU management is standard of care; patient consent is not required except to include patient data. This includes a non-con head CT, point of care labs, blood pressure management and if necessary advanced life support. Conclusion: The Houston MSU treated its first patient on May16,2014, and is carrying out acute stroke treatment within 10-18 minutes of arrival on location.