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

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

Geospatial Techniques for Improved Water Management in Jordan

This research shows a case from Jordan where geospatial techniques were utilized for irrigation water auditing. The work was based on assessing records of groundwater abstraction in relation to irrigated areas and estimated crop water consumption in three water basins: Yarmouk, Amman-Zarqa and Azraq. Mapping of irrigated areas and crop water requirements was carried out using remote sensing data of Landsat 8 and daily weather records. The methodology was based on visual interpretation and the unsupervised classification for remote sensing data, supported by ground surveys. Net (NCWR) and gross (GCWR) crop water requirements were calculated by merging crop evapotranspiration (ETc), calculated from daily weather records, with maps of irrigated crops. Gross water requirements were compared with groundwater abstractions recorded at a farm level to assess the levels of abstraction in relation to groundwater safe yield. Results showed that irrigated area and GCWR were higher than officially recorded cropped area and abstracted groundwater. The over abstraction of groundwater was estimated to range from 144% to 360% of the safe yield in the three basins. Overlaying the maps of irrigation and groundwater wells enabled the Ministry of Water and Irrigation (MWI) to detect and uncover violations and illegal practices of irrigation, in the form of unlicensed wells, incorrect metering of pumped water and water conveyance for long distances. Results from the work were utilized at s high level of decision-making and changes to the water law were made, with remote sensing data being accredited for monitoring water resources in Jordan

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

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

Abstract Number ‐ 245: Machine Learning‐Enabled Detection of Unruptured Cerebral Aneurysms Improves Detection Rates and Clinical Care

Introduction Unruptured cerebral aneurysms (UCAs) have a relatively low prevalence of approximately 3%, but detection can prevent devastating consequences of subarachnoid hemorrhage. Here, we assess the performance of a machine‐learning (ML) algorithm to identify UCAs and determine whether routine use of the algorithm would have improved detection rates and patient care. Methods From a prospectively maintained multi‐center registry across 8 certified stroke centers (1 comprehensive, 7 primary), we identified patients who underwent CT angiogram for evaluation of stroke from 3/14/21 – 11/31/21. An FDA‐cleared convolutional deep neural network (Viz ANEURYSM, Viz.ai, Inc.) trained to identify UCAs at least 4mm analyzed the images. Ground truth was provided by independent expert neuroradiology read. The primary outcome was rate of UCAs detected by the ML algorithm but not detected or addressed in the clinical radiology report or clinical notes, which was determined by two independent researchers. Results Among 1191 CT angiogram scans performed during the study period, 49 were flagged by the ML algorithm as possibly demonstrating an UCA, of which 26 cases were confirmed as true positive (PPV 53%).The most common locations included posterior communicating artery (22%), followed by MCA bifurcation (19%). Of these cases, 9 (35%) were not noted in the clinical radiology report or clinical notes, with a median size of 4.2 mm [IQR 3–7.5 mm], and 22 (85%) were not referred for follow up, with median size of 5 mm [IQR 3.7‐11.3 mm]. Of the 22 cases not referred for follow up, 13 (59%) had been noted in the radiology report. 46% (6/13) of the detected but not referred cases had a diameter greater than 10mm. Conclusions UCAs of sizes and intra‐dural locations that may warrant treatment are frequently missed or not followed up in routine clinical care. An ML algorithm that flags studies and notifies clinicians may minimize missed treatment opportunities