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Background: There is substantial interest in adding endovascular stroke therapy (EST) capabilities in community hospitals. Here, we assess the effect of transitioning to an EST-performing hospital (EPH) on acute ischemic stroke (AIS) admissions in a large hospital system including academic and community hospitals., Methods: From our prospectively collected multi-institutional registry, we collected data on AIS admissions at 10 hospitals in the greater Houston area from January 2014 to December 2022: one longstanding EPH (group A), three community hospitals that transitioned to EPHs in November 2017 (group B), and six community non-EPHs that remained non-EPH (group C). Primary outcomes were trends in total AIS admissions, large vessel occlusion (LVO) and non-LVO AIS, and tissue plasminogen activator (tPA) and EST use., Results: Among 20 317 AIS admissions, median age was 67 (IQR 57-77) years, 52.4% were male, and median National Institutes of Health Stroke Scale (NIHSS) was 4 (IQR 1-10). During the first 12 months after EPH transition, AIS admissions increased by 1.9% per month for group B, with non-LVO stroke increasing by 4.2% per month (P<0.001). A significant change occurred for group A at the transition point for all outcomes with decreasing rates in admissions for AIS, non-LVO AIS and LVO AIS, and decreasing rates of EST and tPA treatments (P<0.001)., Conclusion: Upgrading to EPH status was associated with a 2% per month increase in AIS admissions during the first year post-transition for the upgrading hospitals, but decreasing volumes and treatments at the established EPH. These findings quantify the impact on AIS admissions in hospital systems with increasing EST access in community hospitals., Competing Interests: Competing interests: SAS reports funding from the National Institutes of Health as well as consultancy fees from Penumbra, Viz.AI and Imperative Care for unrelated topics., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: CT Perfusion (CTP) predictions of infarct core play an important role in the determination of treatment eligibility in large vessel occlusion (LVO) acute ischemic stroke (AIS). Prior studies have demonstrated that blood glucose can affect cerebral blood flow (CBF). Here we examine the influence of acute and chronic hyperglycemia on CTP estimations of infarct core., Methods: From our prospectively collected multi-center observational cohort, we identified patients with LVO AIS who underwent CTP with RAPID (IschemaView, Stanford, CA) post-processing, followed by endovascular therapy with substantial reperfusion (TICI 2b-3) within 90 minutes, and final infarct volume (FIV) determination by MRI 48-72 hours post-treatment. Core volume over- and under-estimations were defined as a difference of at least 20 mL between CTP-RAPID predicted infarct core and DWI FIV. Primary outcome was the association of presentation glucose and HgbA1c with underestimation (UE) of core volume and was measured using multivariable logistic regression adjusted for comorbidities and presentation characteristics. Secondary outcomes included frequency of overestimation (OE) of infarct core., Results: Among 256 patients meeting inclusion criteria, median age was 67 [IQR 57-77], 51.6% were female, and 132 (51.6%) and 93 (36.3%) had elevated presentation glucose and elevated HgbA1c, respectively. Median CTP-predicted core was 6 mL [IQR 0-30], median DWI FIV was 14 mL [IQR 6-43] and median difference was 12 mL [IQR 5-35]. Twenty-eight (10.9%) patients had infarct core OE and 68 (26.6%) had UE. Compared to those with no UE, patients with UE had elevated blood glucose (median 119 [103-155] vs 138 [117-195], p=0.002) and HgbA1c (median 5.80 [5.40-6.40] vs 6.40 [5.50-7.90], p=0.009). In multivariable analysis, UE was independently associated with elevated glucose (aOR 2.10, p=0.038) and HgbA1c (aOR 2.37, p=0.012). OE was associated with lower presentation blood glucose (median 109 [ 99-132] in OE vs 127 [107-172] in no OE, p=0.003) and HgbA1c (5.6 [IQR 5.1 - 6.2] in OE vs 5.90 [5.50-6.70] in no OE, p=0.012)., Conclusions: Acute and chronic hyperglycemia were strongly associated with CTP UE in patients with LVO AIS undergoing EVT. Glycemic state should be considered when interpreting CTP findings in patients with LVO AIS., Competing Interests: Competing Interest: None

Importance: The benefit of endovascular stroke therapy (EVT) in large vessel occlusion (LVO) ischemic stroke is highly time dependent. Process improvements to accelerate in-hospital workflows are critical., Objective: To determine whether automated computed tomography (CT) angiogram interpretation coupled with secure group messaging can improve in-hospital EVT workflows., Design, Setting, and Participants: This cluster randomized stepped-wedge clinical trial took place from January 1, 2021, through February 27, 2022, at 4 comprehensive stroke centers (CSCs) in the greater Houston, Texas, area. All 443 participants with LVO stroke who presented through the emergency department were treated with EVT at the 4 CSCs. Exclusion criteria included patients presenting as transfers from an outside hospital (n = 158), in-hospital stroke (n = 39), and patients treated with EVT through randomization in a large core clinical trial (n = 3)., Intervention: Artificial intelligence (AI)-enabled automated LVO detection from CT angiogram coupled with secure messaging was activated at the 4 CSCs in a random-stepped fashion. Once activated, clinicians and radiologists received real-time alerts to their mobile phones notifying them of possible LVO within minutes of CT imaging completion., Main Outcomes and Measures: Primary outcome was the effect of AI-enabled LVO detection on door-to-groin (DTG) time and was measured using a mixed-effects linear regression model, which included a random effect for cluster (CSC) and a fixed effect for exposure status (pre-AI vs post-AI). Secondary outcomes included time from hospital arrival to intravenous tissue plasminogen activator (IV tPA) bolus in eligible patients, time from initiation of CT scan to start of EVT, and hospital length of stay. In exploratory analysis, the study team evaluated the impact of AI implementation on 90-day modified Rankin Scale disability outcomes., Results: Among 243 patients who met inclusion criteria, 140 were treated during the unexposed period and 103 during the exposed period. Median age for the complete cohort was 70 (IQR, 58-79) years and 122 were female (50%). Median National Institutes of Health Stroke Scale score at presentation was 17 (IQR, 11-22) and the median DTG preexposure was 100 (IQR, 81-116) minutes. In mixed-effects linear regression, implementation of the AI algorithm was associated with a reduction in DTG time by 11.2 minutes (95% CI, -18.22 to -4.2). Time from CT scan initiation to EVT start fell by 9.8 minutes (95% CI, -16.9 to -2.6). There were no differences in IV tPA treatment times nor hospital length of stay. In multivariable logistic regression adjusted for age, National Institutes of Health Stroke scale score, and the Alberta Stroke Program Early CT Score, there was no difference in likelihood of functional independence (modified Rankin Scale score, 0-2; odds ratio, 1.3; 95% CI, 0.42-4.0)., Conclusions and Relevance: Automated LVO detection coupled with secure mobile phone application-based communication improved in-hospital acute ischemic stroke workflows. Software implementation was associated with clinically meaningful reductions in EVT treatment times., Trial Registration: ClinicalTrials.gov Identifier: NCT05838456.

Vessel segmentation in fundus images permits understanding retinal diseases and computing image-based biomarkers. However, manual vessel segmentation is a time-consuming process. Optical coherence tomography angiography (OCT-A) allows direct, non-invasive estimation of retinal vessels. Unfortunately, compared to fundus images, OCT-A cameras are more expensive, less portable, and have a reduced field of view. We present an automated strategy relying on generative adversarial networks to create vascular maps from fundus images without training using manual vessel segmentation maps. Further post-processing used for standard en face OCT-A allows obtaining a vessel segmentation map. We compare our approach to state-of-the-art vessel segmentation algorithms trained on manual vessel segmentation maps and vessel segmentations derived from OCT-A. We evaluate them from an automatic vascular segmentation perspective and as vessel density estimators, i.e., the most common imaging biomarker for OCT-A used in studies. Using OCT-A as a training target over manual vessel delineations yields improved vascular maps for the optic disc area and compares to the best-performing vessel segmentation algorithm in the macular region. This technique could reduce the cost and effort incurred when training vessel segmentation algorithms. To incentivize research in this field, we will make the dataset publicly available to the scientific community., (© 2023. Springer Nature Limited.)

Background Prehospital routing of patients with large vessel occlusion (LVO) acute ischemic stroke (AIS) to centers capable of performing endovascular therapy may improve clinical outcomes. Here, we explore whether distance to comprehensive stroke centers (CSCs), stroke severity, and sex are associated with direct-to-CSC prehospital routing in patients with LVO AIS. Methods and Results In this cross-sectional study, we identified consecutive patients with LVO AIS from a prospectively collected multihospital registry throughout the greater Houston area from January 2019 to June 2020. Primary outcome was prehospital routing to CSC and was compared between men and women using modified Poisson regression including age, sex, race or ethnicity, first in-hospital National Institutes of Health Stroke Scale score, travel time, and distances to the closest primary stroke center and CSC. Among 503 patients with LVO AIS, 413 (82%) were routed to CSCs, and women comprised 46% of the study participants. Women with LVO AIS compared with men were older (73 versus 65, P <0.01) and presented with greater National Institutes of Health Stroke Scale score (14 versus 12, P =0.01). In modified Poisson regression, women were 9% less likely to be routed to CSCs compared with men (adjusted relative risk [aRR], 0.91 [0.84-0.99], P =0.024) and distance to nearest CSC ≤10 miles was associated with 38% increased chance of routing to CSC (aRR, 1.38 [1.26-1.52], P <0.001). Conclusions Despite presenting with more significant stroke syndromes and living within comparable distance to CSCs, women with LVO AIS were less likely to be routed to CSCs compared with men. Further study of the mechanisms behind this disparity is needed.

The foveal avascular zone (FAZ) is a retinal area devoid of capillaries and associated with multiple retinal pathologies and visual acuity. Optical Coherence Tomography Angiography (OCT-A) is a very effective means of visualizing retinal vascular and avascular areas, but its use remains limited to research settings due to its complex optics limiting availability. On the other hand, fundus photography is widely available and often adopted in population studies. In this work, we test the feasibility of estimating the FAZ from fundus photos using three different approaches. The first two approaches rely on pixel-level and image-level FAZ information to segment FAZ pixels and regress FAZ area, respectively. The third is a training mask-free pipeline combining saliency maps with an active contours approach to segment FAZ pixels while being trained on image-level measures of the FAZ areas. This enables training FAZ segmentation methods without manual alignment of fundus and OCT-A images, a time-consuming process, which limits the dataset that can be used for training. Segmentation methods trained on pixel-level labels and image-level labels had good agreement with masks from a human grader (respectively DICE of 0.45 and 0.4). Results indicate the feasibility of using fundus images as a proxy to estimate the FAZ when angiography data is not available.

Scarcity of labels for medical images is a significant barrier for training representation learning approaches based on deep neural networks. This limitation is also present when using imaging data collected during routine clinical care stored in picture archiving communication systems (PACS), as these data rarely have attached the high-quality labels required for medical image computing tasks. However, medical images extracted from PACS are commonly coupled with descriptive radiology reports that contain significant information and could be leveraged to pre-train imaging models, which could serve as starting points for further task-specific fine-tuning. In this work, we perform a head-to-head comparison of three different self-supervised strategies to pre-train the same imaging model on 3D brain computed tomography angiogram (CTA) images, with large vessel occlusion (LVO) detection as the downstream task. These strategies evaluate two natural language processing (NLP) approaches, one to extract 100 explicit radiology concepts (Rad-SpatialNet) and the other to create general-purpose radiology reports embeddings (DistilBERT). In addition, we experiment with learning radiology concepts directly or by using a recent self-supervised learning approach (CLIP) that learns by ranking the distance between language and image vector embeddings. The LVO detection task was selected because it requires 3D imaging data, is clinically important, and requires the algorithm to learn outputs not explicitly stated in the radiology report. Pre-training was performed on an unlabeled dataset containing 1,542 3D CTA - reports pairs. The downstream task was tested on a labeled dataset of 402 subjects for LVO. We find that the pre-training performed with CLIP-based strategies improve the performance of the imaging model to detect LVO compared to a model trained only on the labeled data. The best performance was achieved by pre-training using the explicit radiology concepts and CLIP strategy.

Acute ischemic stroke is a leading cause of death and disability in the world. Treatment decisions, especially around emergent revascularization procedures, rely heavily on size and location of the infarct core. Currently, accurate assessment of this measure is challenging. While MRI-DWI is considered the gold standard, its availability is limited for most patients suffering from stroke. Another well-studied imaging modality is CT-Perfusion (CTP) which is much more common than MRI-DWI in acute stroke care, but not as precise as MRI-DWI, and it is still unavailable in many stroke hospitals. A method to determine infarct core using CT-Angiography (CTA), a much more available imaging modality albeit with significantly less contrast in stroke core area than CTP or MRI-DWI, would enable significantly better treatment decisions for stroke patients throughout the world. Existing deep-learning-based approaches for stroke core estimation have to face the trade-off between voxel-level segmentation / image-level labels and the difficulty of obtaining large enough samples of high-quality DWI images. The former occurs when algorithms can either output voxel-level labeling which is more informative but requires a significant effort by annotators, or image-level labels that allow for much simpler labeling of the images but results in less informative and interpretable output; the latter is a common issue that forces training either on small training sets using DWI as the target or larger, but noisier, dataset using CT-Perfusion (CTP) as the target. In this work, we present a deep learning approach including a new weighted gradient-based approach to obtain stroke core segmentation with image-level labeling, specifically the size of the acute stroke core volume. Additionally, this strategy allows us to train using labels derived from CTP estimations. We find that the proposed approach outperforms segmentation approaches trained on voxel-level data and the CTP estimation themselves., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Acute cerebral stroke is a leading cause of disability and death, which could be reduced with a prompt diagnosis during patient transportation to the hospital. A portable retina imaging system could enable this by measuring vascular information and blood perfusion in the retina and, due to the homology between retinal and cerebral vessels, infer if a cerebral stroke is underway. However, the feasibility of this strategy, the imaging features, and retina imaging modalities to do this are not clear. In this work, we show initial evidence of the feasibility of this approach by training machine learning models using feature engineering and self-supervised learning retina features extracted from OCT-A and fundus images to classify controls and acute stroke patients. Models based on macular microvasculature density features achieved an area under the receiver operating characteristic curve (AUC) of 0.87-0.88. Self-supervised deep learning models were able to generate features resulting in AUCs ranging from 0.66 to 0.81. While further work is needed for the final proof for a diagnostic system, these results indicate that microvasculature density features from OCT-A images have the potential to be used to diagnose acute cerebral stroke from the retina.

Background: Prehospital automated large vessel occlusion (LVO) detection in Mobile Stroke Units (MSUs) could accelerate identification and treatment of patients with LVO acute ischemic stroke. Here, we evaluate the performance of a machine learning (ML) model on CT angiograms (CTAs) obtained from 2 MSUs to detect LVO., Methods: Patients evaluated on MSUs in Houston and Los Angeles with out-of-hospital CTAs were identified. Anterior circulation LVO was defined as an occlusion of the intracranial internal carotid artery, middle cerebral artery (M1 or M2), or anterior cerebral artery vessels and determined by an expert human reader. A ML model to detect LVO was trained and tested on independent data sets consisting of in-hospital CTAs and then tested on MSU CTA images. Model performance was determined using area under the receiver-operator curve statistics., Results: Among 68 patients with out-of-hospital MSU CTAs, 40% had an LVO. The most common occlusion location was the middle cerebral artery M1 segment (59%), followed by the internal carotid artery (30%), and middle cerebral artery M2 (11%). Median time from last known well to CTA imaging was 88.0 (interquartile range, 59.5-196.0) minutes. After training on 870 in-hospital CTAs, the ML model performed well in identifying LVO in a separate in-hospital data set of 441 images with area under receiver-operator curve of 0.84 (95% CI, 0.80-0.87). ML algorithm analysis time was under 1 minute. The performance of the ML model on the MSU CTA images was comparable with area under receiver-operator curve 0.80 (95% CI, 0.71-0.89). There was no significant difference in performance between the Houston and Los Angeles MSU CTA cohorts., Conclusions: In this study of patients evaluated on MSUs in 2 cities, a ML algorithm was able to accurately and rapidly detect LVO using prehospital CTA acquisitions.

Background and Purpose: Endovascular therapy (EVT) is a very effective treatment but relies on specialized capabilities that are not available in every hospital where acute ischemic stroke is treated. Here, we assess whether access to and utilization of this therapy has extended uniformly across racial and ethnic groups., Methods: We conducted a retrospective, population-based study using the 2019 Texas Inpatient Public Use Data File. Acute ischemic stroke cases and EVT use were identified using the International Classification of Diseases, Tenth Revision (ICD-10 ) diagnosis and procedure codes. We examined EVT utilization by race/ethnicity and performed patient- and hospital-level analyses. To validate state-specific findings, we conducted patient-level analyses using the 2017 National Inpatient Sample for national estimates. To assess independent associations between race/ethnicity and EVT, multivariable modified Poisson regressions were fitted and adjusted relative risks were estimated accounting for patient risk factors and socioeconomic characteristics., Results: Among 40 814 acute ischemic stroke cases in Texas in 2019, 54% were White, 17% Black, and 21% Hispanic. Black patients had similar admissions to EVT-performing hospitals and greater admissions to comprehensive stroke centers (CSCs) compared with White patients (EVT 62% versus 62%, P =0.21; CSCs 45% versus 39%, P <0.001) but had lower EVT rates (4.1% versus 5.3%; adjusted relative risk, 0.76 [0.66-0.88]; P <0.001). There were no differences in EVT rates between Hispanic and White patients. Lower rates of EVT among Black patients were consistent in the subgroup of patients who arrived in early time windows and received intravenous recombinant tissue-type plasminogen activator (adjusted relative risk, 0.77 [0.61-0.98]; P =0.032) and the subgroup of those admitted to EVT-performing hospitals in both non-CSC (3.0% versus 5.5, P <0.001) and CSC hospitals (7.9% versus 10.4%, P <0.001) while there were no differences between Whites and Hispanic patients. Nationwide sample data confirmed this finding of lower utilization of EVT among Black patients (adjusted relative risk, 0.87 [0.77-0.98]; P =0.024)., Conclusions: We found no evidence of disparity in presentation to EVT-performing hospitals or CSCs; however, lower rates of EVT were observed in Black patients.

Fundus Retinal imaging is an easy-to-acquire modality typically used for monitoring eye health. Current evidence indicates that the retina, and its vasculature in particular, is associated with other disease processes making it an ideal candidate for biomarker discovery. The development of these biomarkers has typically relied on predefined measurements, which makes the development process slow. Recently, representation learning algorithms such as general purpose convolutional neural networks or vasculature embeddings have been proposed as an approach to learn imaging biomarkers directly from the data, hence greatly speeding up their discovery. In this work, we compare and contrast different state-of-the-art retina biomarker discovery methods to identify signs of past stroke in the retinas of a curated patient cohort of 2,472 subjects from the UK Biobank dataset. We investigate two convolutional neural networks previously used in retina biomarker discovery and directly trained on the stroke outcome, and an extension of the vasculature embedding approach which infers its feature representation from the vasculature and combines the information of retinal images from both eyes.In our experiments, we show that the pipeline based on vasculature embeddings has comparable or better performance than other methods with a much more compact feature representation and ease of training.Clinical Relevance-This study compares and contrasts three retinal biomarker discovery strategies, using a curated dataset of subject evidence, for the analysis of the retina as a proxy in the assessment of clinical outcomes, such as stroke risk.

Background: Prior studies on rupture risk of brain arteriovenous malformations (AVMs) in women undergoing pregnancy and delivery have reported conflicting findings, but also have not accounted for AVM morphology and heterogeneity. Here, we assess the association between pregnancy and the risk of intracranial hemorrhage (ICH) in women with AVMs using a cohort-crossover design in which each woman serves as her own control., Methods: Women who underwent pregnancy and delivery were identified using DRG codes from the Healthcare Cost and Utilization Project State Inpatient Databases for California (2005-2011), Florida (2005-2014), and New York (2005-2014). The presence of AVM and ICH was determined using ICD 9 codes. Pregnancy was defined as the 40 weeks prior to delivery, and postpartum as 12 weeks after. We defined a non-exposure control period as a 52-week period prior to pregnancy. The relative risks of ICH during pregnancy were compared against the non-exposure period using conditional Poisson regression., Results: Among 4 022 811 women identified with an eligible delivery hospitalization (median age, 28 years; 7.3% with gestational diabetes; 4.5% with preeclampsia/eclampsia), 568 (0.014%) had an AVM. The rates of ICH during pregnancy and puerperium were 6355.4 (95% CI 4279.4 to 8431.5) and 14.4 (95% CI 13.3 to 15.6) per 100 000 person-years for women with and without AVM, respectively. In cohort-crossover analysis, in women with AVMs the risk of ICH increased 3.27-fold (RR, 95% CI 1.67 to 6.43) during pregnancy and puerperium compared with a non-pregnant period., Conclusions: Among women with AVM, pregnancy and puerperium were associated with a greater than 3-fold risk of ICH., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Coronavirus disease 2019 (COVID-19) has been associated with coagulopathy, and D-dimer levels have been used to predict disease severity. However, the role of D-dimer in predicting mortality in COVID-19 patients with acute ischemic stroke (AIS) remains incompletely characterized. Methods: We conducted a retrospective cohort study using the Optum® de-identified COVID-19 Electronic Health Record dataset. Patients were included if they were 18 or older, had been hospitalized within 7 days of confirmed COVID-19 positivity from March 1, 2020 to November 30, 2020. We determined the optimal threshold of D-dimer to predict in-hospital mortality and compared risks of in-hospital mortality between patients with D-dimer levels below and above the cutoff. Risk ratios (RRs) were estimated adjusting for baseline characteristics and clinical variables. Results: Among 15,250 patients hospitalized with COVID-19 positivity, 285 presented with AIS at admission (2%). Patients with AIS were older [70 (60-79) vs. 64 (52-75), p < 0.001] and had greater D-dimer levels at admission [1.42 (0.76-3.96) vs. 0.94 (0.55-1.81) μg/ml FEU, p < 0.001]. Peak D-dimer level was a good predictor of in-hospital mortality among all patients [c-statistic 0.774 (95% CI 0.764-0.784)] and among patients with AIS [c-statistic 0.751 (95% CI 0.691-0.810)]. Among AIS patients, the optimum cutoff was identified at 5.15 μg/ml FEU with 73% sensitivity and 69% specificity. Elevated peak D-dimer level above this cut-off was associated with almost 3 times increased mortality [adjusted RR 2.89 (95% CI 1.87-4.47), p < 0.001]. Conclusions: COVID-19 patients with AIS present with greater D-dimer levels. Thresholds for outcomes prognostication should be higher in this population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kim, Khose, Abdelkhaleq, Salazar-Marioni, Zhang and Sheth.)

Objective: In patients with large-vessel occlusion (LVO) acute ischemic stroke (AIS), determinations of infarct size play a key role in the identification of candidates for endovascular stroke therapy (EVT). An accurate, automated method to quantify infarct at the time of presentation using widely available imaging modalities would improve screening for EVT. Here, the authors aimed to compare the performance of three measures of infarct core at presentation, including an automated method using machine learning., Methods: Patients with LVO AIS who underwent successful EVT at four comprehensive stroke centers were identified. Patients were included if they underwent concurrent noncontrast head CT (NCHCT), CT angiography (CTA), and CT perfusion (CTP) with Rapid imaging at the time of presentation, and MRI 24 to 48 hours after reperfusion. NCHCT scans were analyzed using the Alberta Stroke Program Early CT Score (ASPECTS) graded by neuroradiology or neurology expert readers. CTA source images were analyzed using a previously described machine learning model named DeepSymNet (DSN). Final infarct volume (FIV) was determined from diffusion-weighted MRI sequences using manual segmentation. The primary outcome was the performance of the three infarct core measurements (NCHCT-ASPECTS, CTA with DSN, and CTP-Rapid) to predict FIV, which was measured using area under the receiver operating characteristic (ROC) curve (AUC) analysis., Results: Among 76 patients with LVO AIS who underwent EVT and met inclusion criteria, the median age was 67 years (IQR 54-76 years), 45% were female, and 37% were White. The median National Institutes of Health Stroke Scale score was 16 (IQR 12-22), and the median NCHCT-ASPECTS on presentation was 8 (IQR 7-8). The median time between when the patient was last known to be well and arrival was 156 minutes (IQR 73-303 minutes), and between NCHCT/CTA/CTP to groin puncture was 73 minutes (IQR 54-81 minutes). The AUC was obtained at three different cutoff points: 10 ml, 30 ml, and 50 ml FIV. At the 50-ml FIV cutoff, the AUC of ASPECTS was 0.74; of CTP core volume, 0.72; and of DSN, 0.82. Differences in AUCs for the three predictors were not significant for the three FIV cutoffs., Conclusions: In a cohort of patients with LVO AIS in whom reperfusion was achieved, determinations of infarct core at presentation by NCHCT-ASPECTS and a machine learning model analyzing CTA source images were equivalent to CTP in predicting FIV. These findings have suggested that the information to accurately predict infarct core in patients with LVO AIS was present in conventional imaging modalities (NCHCT and CTA) and accessible by machine learning methods.

Background: Recent clinical trials have established the efficacy of endovascular stroke therapy and intravenous thrombolysis using advanced imaging, particularly computed tomography perfusion (CTP). The availability and utilization of CTP for patients and hospitals that treat acute ischemic stroke (AIS), however, is uncertain., Methods: We performed a retrospective cross-sectional analysis using 2 complementary Medicare datasets, full sample Texas and 5% national fee-for-service data from 2014 to 2017. AIS cases were identified using International Classification of Diseases , Ninth Revision and International Classification of Diseases , Tenth Revision coding criteria. Imaging utilization performed in the initial evaluation of patients with AIS was derived using Current Procedural Terminology codes from professional claims. Primary outcomes were utilization of imaging in AIS cases and the change in utilization over time. Hospitals were defined as imaging modality-performing if they submitted at least 1 claim for that modality per calendar year. The National Medicare dataset was used to validate state-level findings, and a local hospital-level cohort was used to validate the claims-based approach., Results: Among 50 797 AIS cases in the Texas Medicare fee-for-service cohort, 64% were evaluated with noncontrast head CT, 17% with CT angiography, 3% with CTP, and 33% with magnetic resonance imaging. CTP utilization was greater in patients treated with endovascular stroke therapy (17%) and intravenous thrombolysis (9%). CT angiography (4%/y) and CTP (1%/y) utilization increased over the study period. These findings were validated in the National dataset. Among hospitals in the Texas cohort, 100% were noncontrast head CT-performing, 77% CT angiography-performing, and 14% CTP-performing in 2017. Most AIS cases (69%) were evaluated at non-CTP-performing hospitals. CTP-performing hospitals were clustered in urban areas, whereas large regions of the state lacked immediate access., Conclusions: In state-wide and national Medicare fee-for-service cohorts, CTP utilization in patients with AIS was low, and most patients were evaluated at non-CTP-performing hospitals. These findings support the need for alternative means of screening for AIS recanalization therapies.

Background and Purpose: The optimal endovascular stroke therapy (EVT) care delivery structure is unknown. Here, we present our experience in creating an integrated stroke system (ISS) to expand EVT availability throughout our region while maintaining hospital and physician quality standards., Methods: We identified all consecutive patients with large vessel occlusion acute ischemic stroke treated with EVT from January 2014 to February 2019 in our health care system. In October 2017, we implemented the ISS, in which 3 additional hospitals (4 total) became EVT-performing hospitals (EPHs) and physicians were rotated between all centers. The cohort was divided by time into pre-ISS and post-ISS, and the primary outcome was time from stroke onset to EPH arrival. Secondary outcomes included hospital and procedural quality metrics. We performed an external validation using data from the Southeast Texas Regional Advisory Council., Results: Among 513 patients with large vessel occlusion acute ischemic stroke treated with EVT, 58% were treated pre-ISS and 43% post-ISS. Over the study period, EVT procedural volume increased overall but remained relatively low at the 3 new EPHs (<70 EVT/y). After ISS, the proportion of patients who underwent interhospital transfer decreased (46% versus 37%; P <0.05). In adjusted quantile regression, ISS implementation resulted in a reduction of time from stroke onset to EPH arrival by 40 minutes ( P <0.01) and onset to groin puncture by 29 minutes ( P <0.05). Rates of postprocedural hemorrhage, modified Thrombolysis in Cerebral Infarction (TICI) 2b/3, and 90-day modified Rankin Scale were comparable at the higher and lower volume EPHs. The improvement in onset-to-arrival time was not reflective of overall improvement in secular trends in regional prehospital care., Conclusions: In our system, increasing EVT availability decreased time from stroke onset to EPH arrival. The ISS provides a framework to maintain quality in lower volume hospitals.

Objective: We examine the impact of age and extent of resection (EOR) on overall survival (OS) in geriatric patients with Glioblastoma (GBM)., Methods: The SEER 18 Registries was used to identify patients aged 65 and above with GBM from 2000-2016. Patients were categorized into 4 groups based on EOR: Biopsy/Local Excision (B/LE), Subtotal Resection (STR), Gross Total Resection (GTR), and Supratotal Resection (SpTR). Primary endpoint was OS, which was calculated using the Kaplan-Meier method and analyzed by the Log-rank and Wilcoxon-Breslow-Gehan test. Multivariable Cox proportional hazards regression model was utilized to identify factors associated with OS. Likelihood of undergoing SpTR was explored using a multivariable logistic regression model. Results are given as median [IQR] and HR [95 % CI]., Results: Among 17,820 geriatric patients with GBM, median age was 73 years [68-78], 44 % were female, 91 % White, and 8% Hispanic. SpTR was performed in 2907 (16 %), GTR was performed in 2451 (14 %) patients, STR in 4879 (28 %), and B/LE in 7396 (42 %). There was a decline in the proportion of patients treated with SpTR with advancing age (65-69 years, 17 % vs 95+ years, 0%; p < 0.0001), and older age corresponded with a decrease in the odds of undergoing SpTR. In survival analysis, GTR (HR 0.61 [0.58-0.65]) and SpTR (HR 0.65 [0.62-0.68]) were associated with improved survival, even in octogenarian patients., Conclusions: These findings suggest that aggressive surgical resection is associated with improvement in OS in geriatric patients. These results emphasize that age should not influence surgical strategy, as there is a survival benefit from maximal resection in geriatric patients., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Treatment of ependymoma (EPN) is guided by associated tumor features, such as grade and location. However, the relationship between these features with treatments and overall survival in EPN patients remains uncharacterized. Here, we describe the change over time in treatment strategies and identify tumor characteristics that influence treatment and survival in EPN., Methods and Materials: Using the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) 18 Registries (1973-2016) database, we identified patients with EPN microscopically confirmed to be grade II (EPN-GII) or III (EPN-GIII) tumors between 2004-2016. Overall survival (OS) was analyzed using Kaplan-Meier survival estimates and multivariable Cox proportional hazard models. A sub-analysis was performed by tumor location (supratentorial, posterior fossa, and spine). Change over time in rates of gross total resection (GTR), radiotherapy (RT), and chemotherapy (CS) were analyzed using linear regression, and predictors of treatment were identified using multivariable logistic regression models., Results: Between 2004-2016, 1,671 patients were diagnosed with EPN, of which 1,234 (74 %) were EPN-GII and 437 (26 %) EPN-GIII. Over the study period, EPN-GII patients underwent a less aggressive treatment (48 % vs 27 %, GTR; 60 % vs 30 %, RT; 22 % vs 2%, CS; 2004 vs 2016; p < 0.01 for all). Age, tumor size, location, and grade were positive predictors of undergoing treatment. Univariate analysis revealed that tumor grade and location were significantly associated with OS (p < 0.0001 for both). In multivariable Cox regression, tumor grade was an independent predictor of OS among patients in the cohort (grade III, HR 3.89 [2.84-5.33]; p < 0.0001), with this finding remaining significant across all tumor locations., Conclusions: In EPN, tumor grade and location are predictors of treatment and overall survival. These findings support the importance of histologic WHO grade and location in the decision-making for treatment and their role in individualizing treatment for different patient populations., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Pleomorphic xanthoastrocytomas (PXA) are circumscribed gliomas that typically have a favorable prognosis. Limited studies have revealed factors affecting survival outcomes in PXA. Here, we analyzed the largest PXA dataset in the literature and identify factors associated with outcomes., Methods: Using the Surveillance, Epidemiology, and End Results (SEER) 18 Registries database, we identified histologically confirmed PXA patients between 1994 and 2016. Overall survival (OS) was analyzed using Kaplan-Meier survival and multivariable Cox proportional hazard models., Results: In total, 470 patients were diagnosed with PXA (males = 53%; median age = 23 years [14-39 years]), the majority were Caucasian (n = 367; 78%). The estimated mean OS was 193 months [95% CI: 179-206]. Multivariate analysis revealed that greater age at diagnosis (≥39 years) (3.78 [2.16-6.59], P < .0001), larger tumor size (≥30 mm) (1.97 [1.05-3.71], P = .034), and postoperative radiotherapy (RT) (2.20 [1.31-3.69], P = .003) were independent predictors of poor OS. Pediatric PXA patients had improved survival outcomes compared to their adult counterparts, in which chemotherapy (CT) was associated with worse OS. Meanwhile, in adults, females and patients with temporal lobe tumors had an improved survival; conversely, tumor size ≥30 mm and postoperative RT were associated with poor OS., Conclusions: In PXA, older age and larger tumor size at diagnosis are risk factors for poor OS, while pediatric patients have remarkably improved survival. Postoperative RT and CT appear to be ineffective treatment strategies while achieving GTR confer an improved survival in male patients and remains the cornerstone of treatment. These findings can help optimize PXA treatment while minimizing side effects. However, further studies of PXAs with molecular characterization are needed., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology and the European Association of Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Purpose: Noncontrast head CT and CT perfusion (CTP) are both used to screen for endovascular stroke therapy (EST), but the impact of imaging strategy on likelihood of EST is undetermined. Here, we examine the influence of CTP utilization on likelihood of EST in patients with large vessel occlusion (LVO)., Methods: We identified patients with acute ischemic stroke at 4 comprehensive stroke centers. All 4 hospitals had 24/7 CTP and EST capability and were covered by a single physician group (Neurology, NeuroIntervention, NeuroICU). All centers performed noncontrast head CT and CT angiography in the initial evaluation. One center also performed CTP routinely with high CTP utilization (CTP-H), and the others performed CTP optionally with lower utilization (CTP-L). Primary outcome was likelihood of EST. Multivariable logistic regression was used to determine whether facility type (CTP-H versus CTP-L) was associated with EST adjusting for age, prestroke mRS, National Institutes of Health Stroke Scale, Alberta Stroke Program Early CT Score, LVO location, time window, and intravenous tPA (tissue-type plasminogen activator)., Results: Among 3107 patients with acute ischemic stroke, 715 had LVO, of which 403 (56%) presented to CTP-H and 312 (44%) presented to CTP-L. CTP utilization among LVO patients was greater at CTP-H centers (72% versus 18%, CTP-H versus CTP-L, P <0.01). In univariable analysis, EST rates for patients with LVO were similar between CTP-H versus CTP-L (46% versus 49%). In multivariable analysis, patients with LVO were less likely to undergo EST at CTP-H (odds ratio, 0.59 [0.41-0.85]). This finding was maintained in multiple patient subsets including late time window, anterior circulation LVO, and direct presentation patients. Ninety-day functional independence (odds ratio, 1.04 [0.70-1.54]) was not different, nor were rates of post-EST PH-2 hemorrhage (1% versus 1%)., Conclusions: We identified an increased likelihood for undergoing EST in centers with lower CTP utilization, which was not associated with worse clinical outcomes or increased hemorrhage. These findings suggest under-treatment bias with routine CTP.