Underutilization of Endovascular Therapy in Black Patients With Ischemic Stroke: An Analysis of State and Nationwide Cohorts

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 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%, 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

The Society of Vascular and Interventional Neurology (SVIN) Mechanical Thrombectomy Registry: Methods and Primary Results

Background A better understanding of real‐world practice patterns in the endovascular treatment for large vessel occlusion acute ischemic stroke is needed. Here, we report the methods and initial results of the Society of Vascular and Interventional Neurology (SVIN) Registry. Methods The SVIN Registry is an ongoing prospective, multicenter, observational registry capturing patients with large vessel occlusion acute ischemic stroke undergoing endovascular treatment since November 2018. Participating sites also contributed pre‐SVIN Registry data collected per institutional prospective registries, and these data were combined with the SVIN Registry in the SVIN Registry+ cohort. Results There were 2088 patients treated across 11 US centers included in the prospective SVIN Registry and 5372 in SVIN Registry+. In the SVIN Registry cohort, the median number of enrollments per institution was 160 [interquartile range 53–243]. Median age was 67 [58–79] years, 49% were women, median National Institutes of Health Stroke Scale 16 [10–21], Alberta stroke program early CT score 9 [7–10], and 20% had baseline modified Rankin scale (mRS)≥2. The median last‐known normal to puncture time was 7.7 [3.1–11.5] hours, and puncture‐to‐reperfusion was 33 [23–52] minutes. The predominant occlusion site was the middle cerebral artery‐M1 (45%); medium vessel occlusions occurred in 97(4.6%) patients. The median number of passes was 1 [1–3] with 93% achieving expanded Treatment In Cerebral Ischemia2b50–3 reperfusion and 51% expanded Treatment In Cerebral Ischemia3/complete reperfusion. Symptomatic intracranial hemorrhage occurred in 5.3% of patients, with 37.3% functional independence (mRS0–2) and 26.4% mortality rates at 90‐days. Multivariable regression indicated older age, longer last‐normal to reperfusion, higher baseline National Institutes of Health Stroke Scale and glucose, lower Alberta stroke program early CT score, heart failure, and general anesthesia associated with lower 90‐day chances of mRS0–2 at 90‐days. Demographic, imaging, procedural, and clinical outcomes were similar in the SVIN Registry+. A comparison between AHA Guidelines‐eligible patients from the SVIN Registry against the Highly Effective Reperfusion evaluated in Multiple Endovascular Stroke Trials study population demonstrated comparable clinical outcomes. Conclusions The prospective SVIN Registry demonstrates that satisfactory procedural and clinical outcomes can be achieved in real‐world practice, serving as a platform for local quality improvement and the investigation of unexplored frontiers in the endovascular treatment of acute stroke

Machine Learning Automated Detection of Large Vessel Occlusion From Mobile Stroke Unit Computed Tomography Angiography

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

Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: a Cluster Randomized Clinical Trial

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

Abstract Number ‐ 264: Examining The Impact Of Area Deprivation On Patient Outcomes In LVO AIS

Introduction The Area Deprivation Index (ADI) is a validated neighborhood‐level measure that utilizes variables such as income, education, and employment to quantify relative socioeconomic disadvantages. Here we explore the impact of disparities on EVT access. Methods From our prospectively maintained multi‐hospital registry, we identified patients with LVO AIS from January 2019‐ June 2020. Patient addresses and zip‐codes were validated using US Postal Service codes and matched to census‐tract level ADI scores that were obtained from Neighborhood Atlas. ADI were categorized into high and low using the median ADI as the cuto!. The primary outcome was utilization of EVT and IV tPA and was determined using multivariable logistic regression and expressed as OR [95% CI]. All p‐values are two‐sided with p < 0.05 defined as statistically significant. All analyses were conducted using RStudio (Version 1.2.5001). Results Among 637 patients with LVO AIS, median age was 68, 46% were female, 53% were white, 27% were black, and 78% identified as Hispanic. Median state ADI was 5 IQR [5]. NIHSS was similar between low/high ADI (mean(SD): 13.3(7.75) vs 13.6(8.62), p‐value 0.69) regions. ADI was significantly associated with race (6.41 vs 4, black vs. white, p‐value 0.03). In the univariable analysis, patients treated with EVT had lower mean ADIs (5.2 vs. 4.6, no EVT vs. EVT, p< 0.02). In multivariable analysis adjusted for age, sex, race, ethnicity and NIHSS, higher ADI was significantly associated with greater rates of IV tPA usage (OR 1.7 [1.01‐ 2.98]) but not EVT usage (OR 0.63 [0.04‐1.0]) Conclusions Patients residing in disadvantaged neighborhoods may have reduced rates of reperfusion therapy, despite comparable acute stroke presentation symptoms. These findings are consistent with prior studies demonstrating poorer health outcomes in these populations

Abstract Number ‐ 16: Thrombectomy alone versus Bridging intravenous alteplase in the US population: a pseudo‐randomized controlled trial

Introduction Recent randomized controlled trials (RCT) failed to demonstrate non‐inferiority of skipping IV tPA in patients with planned endovascular therapy (EVT). None of these studies included patients from the US due to regulatory challenges. Given practice patterns vary relative to Asia and Europe, we sought to address this topic using a validated alternative to RCTs, fuzzy regression discontinuity design (RDD). Methods From our prospectively maintained multi‐center registry we identified patients with LVO AIS treated with EVT with and without IV tPA treatment from 1/2018 ‐ 9/2021. We used the time cutoff for IV tPA as our discontinuity and assumed subjects on either side of the cutoff have markedly different probabilities of receiving the treatment but are similar in other relevant characteristics. The primary outcome was good functional outcome defined as 90‐day mRS 0–2 and it was compared between these two populations immediately adjacent to the cutoff using local linear regressions. Results Among 694 patients with LVO AIS who received EVT, median age was 69 [IQR 59‐79], 50%, were female, 44% White, 24% Black, and 14% Hispanic. 51% received IV tPA, with median onset to treatment time of 109 min [IQR 79‐160]. We observed a sharp drop (47%) in the probability of tPA around the cutoff time of 4 hours (allowing 30 minutes for in‐hospital evaluation), while there were no significant differences in other relevant features at the cutoff, validating the underlying RDD assumptions (Figure A). Overall, 33% of patients achieved good functional outcomes and there were no significant differences around the cutoff time (Figure B). In fuzzy RDD, there was no evidence of an association of receiving tPA with good functional outcome with regression discontinuity of only 1.0% (p = 0.98)There were no significant differences in rates of hemorrhage in patients treated with or without IV tPA (22% vs 21%, p = 0.68). Conclusions Our study provides the highest quality US‐based evidence supporting the findings of the outside US trials, demonstrating no benefit of skipping IV tPA in patients with planned EVT

Sentinel lymph node detection rates using indocyanine green in women with early-stage cervical cancer

Objective. Our study objective was to determine feasibility and mapping rates using indocyanine green (ICG) for sentinel lymph node (SLN) mapping in early-stage cervical cancer. Methods. We performed a retrospective review of all women who underwent SLN mapping with ICG during primary surgical management of early-stage cervical cancer by robotic-assisted radical hysterectomy (RA-RH) or fertility-sparing surgery. Patients were treated at two high-volume centers from 10/2012 to 02/2016. Completion pelvic lymphadenectomy was performed after SLN biopsy; additionally, removal of clinically enlarged/suspicious nodes was part of the SLN treatment algorithm. Results. Thirty women with a median age of 42.5 and BMI of 26.5 were included. Most (90%) had stage IB disease, and 67% had squamous histology. RA-RH was performed in 86.7% of cases. One patient underwent fertility sparing surgery. Median cervical tumor size was 2.0 cm. At least one SLN was detected in all cases (100%), with bilateral mapping achieved in 87%. SLN detection was not impacted by tumor size and was most commonly identified in the hypogastric (403%), obturator (26.0%), and external iliac (20.8%) regions. Five cases of lymphatic metastasis were identified (16.7%): three in clinically enlarged SLNs, one in a clinically enlarged non-SLN, and one case with cytokeratin positive cells in an SLN. All metastatic disease would have been detected even if full lymphadenectomy had been omitted from our treatment algorithm, Conclusions. SLN mapping with ICG is feasible and results in high detection rates in women with early-stage cervical cancer. Prospective studies are needed to determine if SLN mapping can replace lymphadenectomy in this setting. (C) 2016 Elsevier Inc. All rights reserved

Effect of COVID‐19 on Acute Ischemic Stroke Hospitalizations and Treatments: Population‐Level Experience

Background Several studies have reported changes in the volume and type of acute ischemic stroke (AIS) hospitalizations during the early stage of the COVID‐19 pandemic. However, population‐based assessments, which include lower volume centers and more comprehensive geographic areas, are limited. Here, we evaluate an entire state‐level experience during the first peak COVID pandemic and compare against a 1‐year prior historical period. Methods We conducted a retrospective population‐based study using the Texas Inpatient Public Use Data File, capturing all discharges from hospitals in the State of Texas, except federal hospitals. AIS admission volumes, patient characteristics, proportions of large vessel occlusion (LVO), admission rates to comprehensive stroke centers, use of intravenous tissue plasminogen activator and endovascular treatment, and patient outcomes were compared between April 1, 2019 and June 30, 2019 (historical control period) and April 1, 2020 and June 30, 2020 (pandemic period). Results A total of 9277 hospitalized AIS cases were identified during the pandemic period, a decrease of 12% (10 524) compared with the control period. Cases without LVO dropped by 15%, whereas LVO cases dropped by only 5%. There were no significant differences in age or race and ethnicity of patients. While admission rates to comprehensive stroke centers (39.6% versus 39.4%, P=0.81) and endovascular treatment use in LVO (17.0% versus 16.3%, P=0.45) were not different between the 2 periods, the use of intravenous tissue plasminogen activator (15.0% versus 13.6%, relative risk [RR], 0.90; 95% CI, 0.84–0.97; P=0.004) decreased. The percentage of patients who died or were discharged to hospice increased from 7.2% to 8.25% (RR, 1.17; 95% CI. 1.06–1.29; P=0.001). Conclusions This study from a statewide population‐level analysis confirms smaller hospital‐based cohorts observing decreasing numbers of milder AIS admissions, and lower use of thrombolysis. Although LVO admissions and endovascular treatment use were largely unchanged, these findings suggest missed treatment opportunities for patients with AIS in the pandemic

Detection of Stroke with Retinal Microvascular Density and Self-Supervised Learning Using OCT-A and Fundus Imaging

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&ndash;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

Quantification of infarct core signal using CT imaging in acute ischemic stroke

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS). In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms “NCCT” and “CTA” to refer to them. This pipeline enables us to answer the questions “Does the injection of bolus enhance the infarct core signal?” and “What is the ideal bolus timing to enhance the infarct core signal?” without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core. The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS. In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors