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

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

Abstract 1122‐000186: Predictors of Functional Outcome in Ischemic Stroke Patients with High Pass Number During Endovascular Therapy

Introduction: The number of thrombectomy passes during endovascular therapy (EVT) for large vessel occlusion (LVO) in acute ischemic stroke (AIS) has been associated with probability of favorable functional outcome, with worse outcomes correlating with greater pass number. While first‐pass recanalization is a strong predictor of functional outcomes, the optimal or maximum recommended number of passes at which patients continue to benefit from EVT remains controversial. Moreover, among patients requiring more attempts, it is unclear if a certain subset of patients continue to benefit despite multiple passes. In this study, we determine predictors of functional outcome among patients requiring a high pass number to achieve successful vessel recanalization. Methods: From our prospectively maintained multi‐institutional registry across 4 comprehensive stroke centers, we identified patients with LVO AIS who underwent EVT requiring ≥ 3 passes to achieve successful reperfusion, defined as ≥ TICI 2b. Patient demographics, co‐morbidities, and severity of stroke based on NIHSS and ASPECTS were included within the analysis. Favorable outcome was defined as 90‐day post‐stroke modified Rankin Scale (mRS) 0–2. The primary outcomes were predictors of favorable outcome, which was assessed by multivariable logistic regression adjusted for age, baseline mRS, NIHSS, admission systolic and diastolic blood pressure, administration of tPA, number of passes during thrombectomy, history of hypertension, hyperlipidemia, atrial fibrillation, coronary artery disease, congestive heart failure, carotid stenosis, and diabetes. Results: Among 116 patients, median age was 70 (IQR 59–80), 48% were female, median NIHSS was 16.5 (IQR 13–22), and median number of passes was 3 (IQR 3–4, range 3–8). Patients with favorable outcome were younger (mean age 63±18.1 vs 70±14.5, favorable vs. non‐favorable, p = 0.041), and had lower NIHSS on presentation (mean 13.9±6.0 vs 18.3±7.4, favorable vs. non‐favorable p = 0.003). Patients with favorable outcome also had lower initial systolic blood pressure (149.6±32.8 vs 163.0±30.0 mmHg, favorable vs. non‐favorable, p = 0.047). In multivariable logistic regression adjusted for demographics and clinical characteristics, lower NIHSS was significantly associated with likelihood of good outcome (OR 0.88, 95% CI 0.81‐0.97, p = 0.009). Conclusions: Patients presenting with lower NIHSS are more likely to benefit from continued EVT attempts. These findings suggest that this population benefits from continued attempts at revascularization

Machine Learning–Enabled Detection of Unruptured Cerebral Aneurysms Improves Detection Rates and Clinical Care

Background Unruptured cerebral aneurysms (UCAs) have a relatively low prevalence of ≈3%, but detection can prevent devastating consequences of subarachnoid hemorrhage. Here, we assess the performance of a machine learning algorithm to identify UCAs and determine whether routine use of the algorithm improves detection and patient care. Methods From a prospectively maintained multicenter registry across 8 certified stroke centers (1 comprehensive and 7 primary), we identified patients who underwent computed tomography angiography for evaluation of possible stroke from March 14, 2021, to November 31, 2021. A convolutional deep neural network (Viz ANEURYSM) trained to identify UCAs at least 4 mm in size analyzed the images, and ground truth was provided by a blinded expert neuroradiologist. The primary outcome was rate of clinical follow‐up for UCAs detected by the machine learning algorithm. Results Among 1191 computed tomography angiograms performed during the study period, 50 (4.2%) were flagged by the machine learning algorithm as possibly demonstrating a UCA, of which 31 cases were confirmed as true positive (positive predictive value, 62%). There were a total of 36 true aneurysms with 4 cases of multiple aneurysms. Overall, the most common locations included internal carotid artery (42%). Of these cases, 10 (27.8%) were not noted in the clinical radiology report or clinical notes, with a median size of 4.4 mm (interquartile range, 1.6 mm), and 24 (67%) were not referred for follow‐up, with median size of 4.4 mm (interquartile range, 4.2 mm). Of the 24 aneurysms not referred for follow‐up, 15 (62.5%) had been noted in the radiology report. A total of 33.3% (5/15) of the detected but not referred cases had a diameter >7 mm, with median PHASES score of 7. Conclusions UCAs of sizes and intradural locations that require attention and may warrant treatment are frequently missed in routine clinical care. A machine learning algorithm that flags studies and notifies clinicians may minimize missed care opportunities

Abstract 073: Thrombectomy Alone Versus Bridging Intravenous Alteplase in A US Population: Regression Discontinuity Analysis

Introduction Recent randomized controlled trials (RCT) failed to demonstrate the 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 that practice patterns vary relative to Asia, Australia, and Europe, we sought to address this topic using a validated alternative to RCTs, regression discontinuity (RD). Methods From the prospectively collected SVIN Registry encompassing all consecutive patients treated with EVT from 12 centers across the US, we identified patients from 12/2010 – 12/2021. RD design achieves quasi‐randomization and can determine causal effects by analyzing subjects on immediately adjacent sides of a cutoff. Here, we take advantage of the sharp drop in the likelihood of IV tPA treatment at the 4.5‐hour mark from the patients’ last known well time. Subjects immediately on either side of the cutoff have markedly different probabilities of receiving the treatment while sharing similar characteristics in other relevant aspects. Patients were excluded if they underwent EVT after inter‐hospital transfer, were inpatient at the time of acute ischemic stroke (AIS), received IV tPA outside the 4.5‐hour window, or were treated with EVT after the 10‐hour window, as these patients would not contribute to the RD analysis. The primary outcome was a good functional outcome defined as 90‐day mRS 0‐2 and was analyzed using validated RD analysis methods with local linear regressions and triangular kernel weights. Secondary endpoints included rates of symptomatic intracranial hemorrhage and substantial reperfusion. Results Among 961 patients who met inclusion criteria, all 12 EVT centers within the SVIN Registry were represented. The median age was 70 [IQR 58‐79], 48% were female, NIHSS was 17, and the most common occlusion location was M1 MCA (45%). There were no substantial differences in presentation characteristics between patients treated with IV tPA and without. We observed a significant fall (30%) in the probability of tPA around the cutoff time of 235 minutes from the last known well to hospital arrival (allowing 35 minutes for in‐hospital evaluation from the 4.5‐hour cutoff), while there were no significant differences in other relevant features including age, NIHSS, ASPECTS, and final reperfusion grade at the cutoff, validating the underlying RD assumptions (Figure A). In RD analysis, we observed no association between IV tPA treatment and functional independence at 90‐days in patients undergoing EVT (risk difference ‐0.29 (95% CI [‐2.75 to 2.15]) (Figure B), nor in the secondary outcomes of excellent outcomes (mRS 0‐1) at 90 days, mortality, symptomatic ICH or first pass reperfusion. Conclusion Here, we perform what we believe to be the strongest study to date examining the benefit of IV tPA in patients undergoing EVT for LVO AIS in a US‐based cohort, and we find no evidence of association with 90‐day functional independence, consistent with findings of RCTs performed outside the US