Risultati per: Stroke

Stroke, Volume 56, Issue Suppl_1, Page ATMP31-ATMP31, February 1, 2025. Background:A prehospital, paramedic-administered scale to distinguish intracerebral hemorrhage (ICH) from acute cerebral ischemia (ACI) could improve routing to appropriate centers, enrich field randomized trials with targeted subtype patients, and potentially guide prehospital clinical treatment such as hyperacute blood pressure (BP) lowering. We aimed to create a quickly administered prehospital scale from prospectively performed field assessments.Methods:Two scales were created from NIH Field Administration of Stroke Therapy Magnesium (FAST-MAG) trial data, using logistic regression model with backward stepwise variable selection and retention criterion of p

Stroke, Volume 56, Issue Suppl_1, Page ATP326-ATP326, February 1, 2025. Background:In patients with ischemic stroke who receive insertable cardiac monitors, the CRYSTAL-AF and STROKE-AF randomized trials showed similar (~12%) AF detection rates at one year among patients with cryptogenic stroke and stroke from large vessel disease and small vessel disease, respectively. This finding may suggest that AF detected on an insertable cardiac monitor in cryptogenic stroke is often not pathogenic. In this study, we aim to compare AF detection rates on ZIOPATCH in ESUS vs. non-cardioembolic ischemic stroke.Methods:From a comprehensive stroke center registry, we identified patients with a diagnosis of ischemic stroke who underwent a ZIOPATCH to look for AF. The primary predictor was stroke subtype (ESUS vs. non-cardioembolic). Non-cardioembolic stroke was defined as stroke from large artery atherosclerosis, small vessel disease, or other known mechanism. ESUS was defined using the ESUS consensus criteria. The study outcome was AF detected on ZIOPATCH. We compared baseline characteristics and risk factors between patients with vs. without AF detected. Univariate and multivariate regression models were used to determine odds ratios (OR).Results:We identified 478 patients who had a ZIOPATCH placed; 410 had the ZIOPATCH completed (217 ESUS, 71 small vessel disease, 100 large vessel disease, and 22 had another defined mechanism). The mean age was 69 years and 51% were men. The mean duration of ZIOPATCH monitoring was 12 days and 17 (4.1%) had AF. Patients with AF were more likely to be older (82 years vs. 68 years, p

Stroke, Volume 56, Issue Suppl_1, Page ATP316-ATP316, February 1, 2025. Background:Despite improvements in the safety of transcatheter aortic valve replacement (TAVR), ~4% of patients experience a procedure-related stroke. TAVR seems to be preferred in patients with cancer; however, related research on the clinical efficacy and stroke outcomes of TAVR in patients with cancer is limited. This study aimed to evaluate the association of TAVR-related stroke in patients with cancer and without cancer.Methods:This retrospective cohort study identified 14,046 patients with aortic valve stenosis that underwent TAVR. Patients were stratified into two groups: cancer patients 13583 (96.7%) and non-cancer patients 463 (3.3%). We assessed if there was a difference in stroke events between cancer and non-cancer patients. Binary logistic regression models were used to measure the stroke association.Results:Among 14,046 TAVR patients, 214 (1.5%) had a procedure-related stroke. These patients were more likely to be female (p=0.0007, 95% CI: 1.219-2.102), older (p

Stroke, Volume 56, Issue Suppl_1, Page ATMP37-ATMP37, February 1, 2025. Introduction:Limited studies have examined racial disparities in long-term survival after acute ischemic stroke (AIS) with inconsistent findings. We examined these disparities among Medicare fee-for-service (FFS) beneficiaries in U.S.Methods:We analyzed data on 1,997,487 Medicare FFS beneficiaries aged ≥65 years hospitalized with incident AIS (ICD-10 code I63) and survived >30 days from January 1, 2000 to December 31, 2017, and were followed-up until December 31, 2022. Cox proportional hazard models estimated the adjusted hazard ratio (aHR, 95% CI) and adjusted survival curves by race/ethnicity (non-Hispanic White (White), non-Hispanic Black (Black), Hispanic and Other). Models were adjusted for age, sex, and comorbidities.Results:The median age at AIS hospitalization was 78 years (IQR 72.0-84.0); 57.0% were women; 81.8%, 10.8%, 4.8% and 2.6% were White, Black, Hispanic and Other, respectively. Over a median follow-up of 4.9-years (IQR 1.7-8.8), there were 1,738,452 all-cause deaths. Adjusted 5-year survival after AIS improved from 2000-2004 to 2015-2017 for White (46.5% (95% CI 46.4-46.6) to 50.9% (50.7-51.1)), and Black (46.0% (45.8-46.3) to 48.9% (48.3-49.2)). For Hispanic and Other, survival remained largely unchanged: 54.4% (54.1-54.8) to 54.2% (53.6-54.8)) for Hispanic and 55.9% (55.4-56.4) to 54.7% (54.0-55.5) for Other. A clear pattern of long-term survival after AIS emerged by race/ethnicity showing similar survival between Hispanic and Other and between White and Black people (Figure). Stroke mortality risk was ~25% higher for White and Black compared to Hispanic and Other (aHR 1.25 (1.24-1.26)). This pattern was consistent across age groups and sex.Conclusions:Long-term survival after AIS has improved for White and Black Medicare FFS beneficiaries over time, while it remained largely unchanged for Hispanic and Other groups. This indicates persistent racial disparities in stroke outcomes.

Stroke, Volume 56, Issue Suppl_1, Page ATMP41-ATMP41, February 1, 2025. Background:Stroke remains a leading cause of mortality and morbidity, with treatment and outcomes differing across ethnic groups. Tele-stroke networks have expanded access to stroke care, particularly in underserved areas, but the impact of ethnicity on these outcomes is still being explored. This study aims to compare stroke treatment outcomes among White/Caucasian, African-American, and Other ethnic groups within a tele-stroke network.Methods:We conducted a retrospective analysis of 4,256 stroke patients treated within a tele-stroke network spanning 38 hospitals. Patients were categorized into three ethnic groups: White/Caucasian (n = 2,925), African-American (n = 1,122), and other (n = 209). Data on baseline characteristics, treatment interventions, and discharge outcomes were analyzed using chi-square tests for categorical variables and Kruskal-Wallis tests for continuous variables.Results:The median age was highest in White/Caucasian patients (70 years, IQR 57-81) and lowest in the other group (61 years, IQR 49-72) (p < 0.001). True strokes occurred in 82.3% of White/Caucasian patients, 79.3% of African-American patients, and 89.9% of the other group (p = 0.001). tPA was administered to 19.4% of White/Caucasian patients, 16.7% of African-American patients, and 15.2% of the other group (p = 0.12). Mechanical thrombectomy rates were similar across all groups (p = 0.07). Discharge to home was most common in the other group (58.4%), followed by African-American (50.3%) and White/Caucasian patients (44.9%) (p = 0.01).Conclusion:This study identified important ethnic differences in stroke treatment and outcomes within a tele-stroke network. While there were no significant disparities in the administration of key treatments such as tPA and mechanical thrombectomy, differences in age, gender distribution, stroke severity, and true stroke rates highlight the need for tailored approaches in stroke management to address the unique needs of different racial/ethnic groups.

Stroke, Volume 56, Issue Suppl_1, Page ATMP29-ATMP29, February 1, 2025. Introduction:Mobile Stroke Treatment Units (MSTU) are improving clinical outcomes by significantly shortening the time-to-treatment. On scene, the specialized staff, including a registered nurse and critical care paramedic, are able to rapidly assess the patient for acute stroke symptoms. This rapid treatment algorithm can potentially lead to missed strokes as compared to those patients transported via standard Emergency Medical Services (EMS). Therefore, we evaluated the rate of missed stroke diagnosis through MSTU assessment in the field as compared to the standard EMS-to-Emergency Department (ED) pathway.Methods:A retrospective analysis of patients with potential stroke evaluated by the MSTU crew between 7/25/23 and 5/31/24 was performed. Admission criteria includes age 18 years or older, last known well within 24 hours, one or more cardinal signs of stroke, and exclusions of any other suspected diagnosis outside of stroke or neurological disorders. Our study evaluated patients who were excluded from MSTU admission based on failure to meet said admission criteria. These patients were transported to the hospital by standard EMS. Patient demographics, symptoms, and final hospital diagnoses were extracted to complete univariate and multivariable regression analyses.Results:Our final analysis included 235 patients (mean age 66.3, 56.0% women) evaluated by the MSTU crew, but not admitted due to lower concern for stroke. In our study cohort, 11 had a final diagnosis of stroke that were missed upon initial MSTU evaluation. Patients with missed stroke were significantly older (OR 1.04 (95%CI 1.01 – 1.08), p=0.04) and had a nonsignificant trend towards female sex (OR 0.44 (95%CI 0.14 – 1.45), p=0.17). We did not see any differences among specific MSTU staff performing the evaluation or time of day and day of week. Of the 11 patients with a final stroke diagnosis, none received any acute interventions on arrival to the hospital.Conclusion:Our MSTU admission criteria resulted in an overall low rate of true stroke misses with none of the “missed strokes” receiving an acute intervention at the hospital. Rapid patient evaluation on scene by an expert stroke crew adhering to a standardized admission criterion does not lead to acute stroke interventions being performed upon arrival to the ED. Older patients with stroke-like symptoms evaluated by a Mobile Stroke Treatment Unit are more likely to be misdiagnosed, which deserves further investigation.

Stroke, Volume 56, Issue Suppl_1, Page ATMP34-ATMP34, February 1, 2025. Introduction:Stroke misdiagnosis is not uncommon and Black people have a higher odds of stroke misdiagnosis than White people. Little is known about the reasons for misdiagnosis or how diagnostic pathways lead to error.Hypotheses:To explore possible explanations for race differences in stroke diagnosis, we examine two hypotheses. First, to understand where diagnostic pathways may err we quantified, whether race predicted whether receipt of initial imaging (ie. CT/MRI), hypothesizing that black people receive less imaging. Second, amongst individual that receive imaging, we hypothesized that Black people will be less likely to receive stroke diagnoses or be admitted to the hospital.Methods:We used data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2011-2021. Our primary study population was adults with any neurologic chief complaint, defined using reason for visit (RFV) data. Our primary exposure was Black race. For each outcome, we built unadjusted, minimally adjusted, and fully adjusted survey-weighted logistic regression models to predict outcomes: receipt of imaging, stroke diagnoses and hospital admission. The minimally adjusted model added age, sex, and insurance to race/ethnicity. The fully adjusted model added vital signs, arrival by ambulance, triage level, episode of care, vascular risk factors, prior vascular disease and RFV-based characterization of stroke probability.Results:The mean age was across the ~146,000 emergency room visits 47.8 ± 19.7 and black people comprised 24.9% of the sample. Amongst individuals with a neurologic RFV, black people were less likely to get a CT (OR 0.79, 95% CI 0.72-0.88), MRI (OR 0.64, 95% CI 0.47-0.89), or any imaging (OR 0.78, 95% CI 0.71-0.86 ) in the unadjusted model. These effects persisted or were strengthened in the fully adjusted model: Black race-imaging association (OR 0.67, 95% CI 0.45-0.99). Conditioned on receiving imaging after full adjustment, there was no association between Black race and stroke diagnoses (OR 1.1, 95% CI 0.63-1.9) or hospital admission for stroke (OR 1.19, 95% CI 0.46-3.08).Conclusion:We found that Black people were less likely to receive imaging when presenting with neurologic reasons for visit, but that conditioned on receiving imaging, there were no differences in stroke diagnosis or hospitalization. This suggests that race differences in stroke diagnosis likely occur at or prior to the time of initial imaging selection.

Stroke, Volume 56, Issue Suppl_1, Page ATMP47-ATMP47, February 1, 2025. Background:Life’s Essential 8 (LE8) is a cardiovascular health (CVH) metric proposed by the American Heart Association (AHA) that includes blood glucose, blood pressure, lipid levels, diet, physical activity, nicotine exposure, body mass index, and sleep duration. LE8 is used as a tool to assess and improve CVH outcomes. Little is known about the association between LE8 and mortality after stroke.Methods:We included data from participants aged 20 and older with self-reported stroke who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Data was linked with National Death Index mortality data through 2019. The association between poor and ideal LE8 scores and all-cause mortality and CV mortality rates after stroke were determined in 3 models: (1) unadjusted; (2) adjusted for sociodemographic factors (age, sex, race/ethnicity, poverty income ratio (PIR)) and (3) further adjusted for comorbidities (Charlson Comorbidity Index).Results:During the study period, 1,019 out of 26,670 individuals reported a prior stroke. When assessing mortality in those with poor CVH, although the direction of the hazard ratios (HR) suggested an increased risk of CV mortality with higher numbers of poor LS8 metrics, the associations were not significant in any of the models. However, for all-cause mortality, the risk increased with a higher number of poor CVH metrics. Possessing at least 4 poor CVH metrics was associated with a higher risk of all-cause mortality (Model 1 HR 1.69, 95%CI 1.13-2.52; Model 2 HR 2.25, 1.51-3.35; Model 3 HR 2.10, 1.39-3.20). Conversely, possessing at least 4 ideal metrics was associated with lower CV mortality in the unadjusted model, (HR 0.35, 0.15-0.85) but this association was no longer significant in Models 2 and 3. Possessing at least 4 ideal CVH characteristics was associated with lower all-cause mortality in all 3 models (Model 1 HR 0.50, 0.29-0.85; Model 2 HR 0.54, 0.31-0.92; Model 3 HR 0.56, 0.33-0.96). Higher LE8 scores were associated with lower CV mortality in Model 2 (HR per point LE8 score 0.98, 0.96-0.99) and Model 3 (0.98, 0.96-0.99) and all-cause mortality in Model 2 (0.98, 0.97-0.99) and Model 3 (0.98, 0.97-0.99).Conclusion:A higher LE8 score correlates with an increased risk of both CV and all-cause mortality in stroke survivors. Possession of at least 4 ideal CVH metrics is associated with a nearly two-thirds risk reduction in CV mortality and 50% risk reduction in all-cause mortality.

Stroke, Volume 56, Issue Suppl_1, Page ATMP36-ATMP36, February 1, 2025. This study compares the accuracy of manual stroke scale calculations against electronically calculated scores using the Ultimate Stroke Scale (USS), a new software designed to automate large vessel occlusion (LVO) screening scales from a modified National Institute of Health Stroke Scale (NIHSS). The USS has the potential to streamline LVO screening with enhanced accuracy using multiple validated stroke scales simultaneously.We prospectively applied eight stroke screening scales (NIHSS, BE-FAST, VAN, LAMS, FAST-ED, EMS RACE, 3-ISS, and PASS) to 199 stroke activations between January 2021 to December 2023. These values were recalculated with the USS calculator which incorporates inputs from a modified NIHSS score, including up to two additional points for hand grip strength. A Bland-Altman analysis was conducted to assess agreement between manual and USS-calculated scores.The NIHSS showed a percentage error of -8.24% and a mean difference of -0.97 (LoA: -3.88 to 1.93). The BE-FAST scale exhibited a percentage error of -14.72% and a mean difference of -0.12 (LoA: -0.85 to 0.60). The VAN scale had a percentage error of -21.76% and a mean difference of -0.11 (LoA: -0.83 to 0.62). The LAMS scale had a percentage error of 6.59% and a mean difference of 0.15 (LoA: -1.20 to 1.50). The FAST-ED scale had a percentage error of -4.82% and a mean difference of -0.15 (LoA: -2.10 to 1.80). The EMS-RACE scale had a percentage error of -9.99% and a mean difference of -0.39 (LoA: -3.20 to 2.42). The 3-ISS scale exhibited the highest percentage error of -29.36% and a mean difference of -0.54 (LoA: -2.47 to 1.39). The PASS scale had the lowest percentage error at -2.86% and a mean difference of -0.04 (LoA: -0.66 to 0.58). The combined percentage error for all scales was -8.44%, increasing slightly to -8.61% when excluding the NIHSS score. Excluding both NIHSS and 3-ISS reduced the combined error to -5.44%.Our findings demonstrate a general agreement between the manual and USS-calculated scores, with the strongest concordance observed in PASS, FAST-ED, and LAMS. Although some scales exhibited larger discrepancies, the moderate overall combined percentage error suggests that USS-calculated scores are generally consistent with manual calculations. These findings support the potential of the USS software to streamline LVO stroke screening, although further validation is necessary.

Stroke, Volume 56, Issue Suppl_1, Page AWP187-AWP187, February 1, 2025. Introduction:3 to 5% of patients undergoing endovascular thrombectomy present impossible catheter access to the occlusion site from transfemoral access (TFA), largely attributed to complex arterial anatomy. Radial access can be an effective bailout strategy, but intraprocedural delays may negatively impact outcomes. Novel image processing algorithms allow for advanced characterization of vascular pathways from baseline neuroimaging, enabling the exploration of predictive models of impossible TFA before arterial puncture.Methods:A retrospective cohort of patients with an anterior large vessel occlusion who received thrombectomy from TFA between 2017 and 2023 were included in this study. A previously described automatic vascular analysis software was used to generate centerline graphs from the aorta to the intracranial occlusion site from baseline CTA. ArterialGNet, a graph neural network based on graph attention designed to integrate descriptors of centerline pathways extracted at three different distance scales, was trained for impossible TFA prediction. Five-fold cross validation was used for model derivation. The method was compared to a previously introduced random forest ensemble model with extreme gradient boosting (XGBRF) based on six vascular tortuosity descriptors of the aortic and supra-aortic regions.Results:A total of 745 patients (aged 78 years IQR 68-85, 56% women) were included in this study. Patients treated between 2017 to 2022 (n=568, 3.2% with impossible TFA) were used for model training and validation. Patients treated in 2023 (n=177, 3.4% with impossible TFA) were held out for testing. In validation, the best-performing configuration of ArterialGNet achieved a C-statistic of 0.82 (95%CI 0.74-0.90), similar to the baseline model (0.82, 95%CI 0.77-0.88). Comparable outcomes were observed in the final testing for ArterialGNet (0.84, 95%CI: 0.82–0.86). In contrast, the XGBRF model exhibited signs of overfitting (0.65, 95% CI: 0.53–0.78). In final testing, ArterialGNet predicted impossible TFA with a sensitivity of 0.80 (95%CI 0.66-0.94) and a specificity of 0.84 (95%CI 0.76-0.91). Median processing time for ArterialGNet was below 4 min.Conclusions:A novel model for impossible TFA prediction was validated with a large dataset. Impossible TFA prediction before arterial puncture may assist in decision support for initial access selection in thrombectomy, reducing intraprocedural delays and potentially improving clinical outcomes.

Stroke, Volume 56, Issue Suppl_1, Page ATP306-ATP306, February 1, 2025. Background:Cancer is a leading cause of mortality and a well-known risk factor for ischemic stroke. However, the relationship between cancer and stroke is not well studied. Previous research in this area suggests presence of right-to-left shunt as a possible underlying mechanism of paradoxical embolism in patients with cancer diagnosis within one year of the stroke. Thus, our study seeks to further investigate the potential role of right-to-left shunting in stroke occurrence among cancer patients.Methods:This is a retrospective cohort study with our population consisting of patients presenting to the Ottawa Hospital with ischemic stroke between January 01, 2020, and December 31, 2022, who have undergone transthoracic echocardiography. Presence of right-to-left shunting is identified on echocardiography in patients without cancer and those with cancer diagnosis one year before and one year after the ischemic stroke. The prevalence of shunt is assessed using 95% confidence intervals (CI).Results:Among 495 patients (37% female, median age 53 years) presenting with ischemic stroke, 47 (9.5%) had cancer diagnosis within one year of stroke, with 12 patients (25.5%, 95% CI 14 – 40) diagnosed with a shunt. In contrast, among 448 patients (90.5%) that did not have a cancer diagnosis within one year of their stroke, 133 patients (30%, 95% CI 25 – 34) were identified to have a shunt.Conclusion:The prevalence of right-to-left shunting tends to be lower in patients presenting with ischemic stroke and active cancer diagnosis. This result is consistent with a recent study in this area indicating a higher rate of shunt among patients without cancer than those with cancer. Our finding does not support the hypothesis that cancer-associated stroke is related to right-to-left shunting.

Stroke, Volume 56, Issue Suppl_1, Page ATMP32-ATMP32, February 1, 2025. Introduction:Facilitating evidence-based uptake of new medication regimens for disease prevention is a well-recognized public health challenge. Using data from GWTG-Stroke, researchers previously reported that, after minor ischemic stroke (NIHSS 0-3), the use of aspirin-clopidogrel for stroke prevention is highly variable despite guideline recommendations. We sought to explore potential changes in dual antiplatelet therapy (DAPT) use in patients with moderate ischemic stroke (NIHSS 4-5) after the publication of the THALES (The Acute Stroke or Transient Ischemic Attack Treated With Ticagrelor and ASA for Prevention of Stroke and Death) trial in 2020.Methods:We used the GWTG-Stroke registry to describe patterns of DAPT use in the U.S. from 2019 to 2023. All patients with a final diagnosis of ischemic stroke, NIHSS 4-5, hospital arrival within 24 hours, who lacked an indication for anticoagulation (e.g., atrial fibrillation) and were not treated with thrombolysis/thrombectomy were included in our study. Patients with NIHSS 4-5 (moderate stroke) were not included in prior randomized controlled trials of aspirin-clopidogrel for short-term stroke prevention but were included in THALES. We reported basic demographic features of our cohort and used the Cochran-Armitage trend test to report changes in aspirin-ticagrelor use by year.Results:We identified a total of 40,624 acute ischemic stroke patients with NIHSS 4-5 during the study period. The mean age was 68 years and 47% of patients were women. We found that a total of 20,293 (50%) patients were discharged on aspirin-clopidogrel whereas 1,335 (3.5%) were discharged on aspirin-ticagrelor. The use of both DAPT regimens significantly increased over time (Figure 1, p

Stroke, Volume 56, Issue Suppl_1, Page AWMP120-AWMP120, February 1, 2025. Introduction:We previously demonstrated that Mucosal-associated Invariant T (MAIT) cells were involved in acute ischemic stroke by regulating neuroinflammation (JAHA 2021). This study aimed to clarify the dynamics and role of circulating peripheral MAIT cells in acute ischemic stroke patients.Methods:We enrolled patients with acute ischemic stroke who admitted to Jichi Medical University Hospital, classifying them into severe (NIHSS ≥10) and mild (NIHSS

Stroke, Volume 56, Issue Suppl_1, Page ATMP30-ATMP30, February 1, 2025. Background:The prehospital scales have been developed to identify stroke patients with large vessel occlusion (LVO) to facilitate rapid transport to appropriate stroke centres. In practice, these stroke scales have moderate accuracy. There is a pressing need for adjunct easy-to-use and interpret diagnostic devices to improve prehospital stroke diagnosis and LVO detection. We aim to determine whether a machine learning algorithm using adjunct electroencephalography (EEG) Spectra can improve the accuracy of LVO detectionMethods:Adult patients with suspected acute stroke were prospectively enrolled as soon as possible on arrival at the emergency department. A wearable MuseTMheadband (InteraXon Inc, Canada) with an embedded 4-channel EEG was used for a resting 3-minute recording. EEG Spectra including relative alpha, beta, theta and delta spectral powers, delta-alpha ratio (DAR) and pairwise-derived brain symmetry indices (pdBSI) were calculated. These indices were compared between patients with LVO and non-LVO groups. The accuracy of LVO detection was tested with the aid of supervised machine learning(ML) algorithms including EEG Spectra, Los Angeles Motor Stroke Scale (LAMS), sex and side of stroke.Results:A total of 142 patients were included in the analysis with a mean age of 69.6±13.7 years, 60(42.2%) females, (Stroke Subtype:113[79.6%] were ischemic stroke, 22[15.5%] stroke mimics, 7[4.9%] intracerebral hemorrhage) and median NIHSS 5(2-11). Thirty-seven(26.1%) patients had LVO and EEG was acquired at a median of 6h 45m (3h 29m – 14h 15m) after symptom onset. Relative alpha spectral power was lower in both affected (p

Stroke, Volume 56, Issue Suppl_1, Page AWP186-AWP186, February 1, 2025. Background and Purpose:Primary aldosteronism (PA) is characterized by the autonomous overproduction of aldosterone leading to the risk of occurrence of acute ischemic stroke (AIS), but the exact prevalence of PA is unknown in patients with AIS. PA induces oxidative stress and inflammation through vascular endothelial cells, which may damage small vessel disease (SVD). We conducted a prospective study to investigate the prevalence of screening and definite diagnosis of PA in patients with AIS. Next, we aimed to reveal whether SVD markers could be associated with PA.Methods:We screened consecutive patients with AIS who participated in our prospective study to investigate the prevalence of PA and followed up for PA evaluation from October 2020 to December 2022. Inclusion criteria were patients with AIS hospitalized and diagnosed with hypertension. Exclusion criteria were patients taking medications affecting renin, aldosterone, and catecholamines. The screening criteria for PA was defined as the aldosterone-to-renin ratio > 200. Final diagnosis of PA was judged by endocrinologist if one of the captopril challenge test, saline infusion test, and furosemide-upright test was positive following discharge. We evaluated total SVD score based on white matter hyperintensities (separately scored by periventricular hyperintensity [PVH] and deep and subcortical white matter hyperintensity), cerebral microbleeds (CMBs; categorized into deep, lobar, and infratentorial lesions), enlarged perivascular spaces (separately scored in basal ganglia and centrum semiovale), and old lacunes on MRI.Results:We included 120 patients with AIS (93 [78%] male, median age 62 years, Figure 1). The screening for PA was positive in 33 (28%) patients and 8 (7%) patients were finally diagnosed with definite PA. In Poisson regression analysis with a robust variance estimator, total SVD score was related to positive PA screening (prevalence ratio [PR] 1.261, 95% CI 1.021-1.556,p= 0.031) and definite PA diagnosis (PR 1.946, 95% CI 1.229-3.082,p= 0.005, Figure 2). In terms of each SVD marker, severe PVH, and deep and lobar CMBs were associated with positive PA screening and definite PA diagnosis (Figure 3).Conclusions:Twenty-eight percent of patients with AIS were positive for PA screening, and then about a quarter of them were confirmed as definite PA. SVD burden, especially PVH, and deep and lobar CMBs, might be associated with positive screening and definite diagnosis of PA.

Stroke, Volume 56, Issue Suppl_1, Page ATP312-ATP312, February 1, 2025. Background/Objectives:Perioperative stroke is a significant cause of morbidity and mortality in patients undergoing cardiac, vascular, and neurosurgical procedures. We assessed the rate, characteristics, risk factors and survival outcomes of perioperative stroke following surgical resection of glioma.Design:This is a retrospective chart review of a single quaternary care center of patients with glioma between 2005-2021 who underwent resection. Stroke within 30 days of surgical resection was identified based on the radiology read of MRI brain for ischemic stroke and CT brain for hemorrhagic stroke that was obtained as part of clinical care. This was then confirmed retrospectively by a neurologist who reviewed imaging and medical records, excluding expected post-operative changes. Descriptive analysis and logistic regression were conducted. Overall survival was estimated with Kaplan-Meier methods from the date of surgery to death and compared with the log rank test.Results:Out of 738 patients who underwent surgical resection of their glioma and underwent brain MRI or CT head, 20 (2.71%) had radiographic evidence of strokes, with the mean (SD) time from surgery to stroke 5.4 (16.2) days. Of these, 13 (65%) had ischemic strokes, 7 (35%) had hemorrhagic strokes. Out of all perioperative strokes, 9 (45%) were symptomatic (total incidence of 1.2%), and 11 (55%) were asymptomatic. Patients who had a stroke were older [mean (SD); 60.4 (13.7) vs. 52.8 (15.0) years; p=0.026], had a higher rate of atrial fibrillation (p= 0.002), and had comorbid hyperlipidemia (p=0.039) and hypertension (p=0.047). Descriptive analysis of this cohort is summarized in Table 1. Older age, carrying a diagnosis of atrial fibrillation, and having hyperlipidemia were associated with higher odds of having a perioperative stroke (Table 2). In an attempt to generate a multivariate logistic model, stepwise selection yielded no significant results likely due to the low number of strokes in this cohort. The median survival for patients with stroke was 24.6 months (95% CI:21.8-32.1), which was lower than for patients who did not suffer a stroke (29.3 months, 95% CI: 25.6-32.9) (p=0.052).Conclusion:Older age, atrial fibrillation, hyperlipidemia, and hypertension were associated with perioperative stroke risk after glioma resection. Future studies should evaluate underlying mechanisms and stroke etiologies to better identify high risk patients.