English, Stephen

The effect of induced hypertension (iHTN) on functional outcomes in noncardioembolic acute ischemic stroke (AIS) with early neurological deterioration remains unclear. We performed a systematic review and meta-analysis evaluating the safety and efficacy of iHTN vs medical management. A literature search of PubMed/MEDLINE, Web of Science, Ovid Embase, and Scopus from inception through November 6th, 2025. The protocol was registered in PROSPERO (CRD420261282211). Studies evaluating iHTN within 72 h of AIS onset and reporting 90-day functional independence (modified Rankin Scale [mRS] 0-2) were included. Safety outcome was hemorrhagic transformation. Pooled risk ratios (RRs) with 95% confidence intervals (CIs) were estimated using random-effects model based on Restricted Maximum-Likelihood (REML) methods. Three studies were included, comprising 366 patients (iHTN, n = 180; control, n = 186). Mean age was 65.8 ± 11.8 years, 60.9% were men, and the mean initial NIHSS was 5.4 ± 3.5. Target systolic blood pressure (SBP) was a 15-25% increase from baseline SBP using phenylephrine. Functional independence at 90-days occurred more often in the iHTN group than in controls (68.8% vs 50.5%; RR = 1.37; 95% CI, 1.09-1.73; P = 0.007). Hemorrhagic transformation was uncommon and did not differ significantly between groups (3.3% vs 0.5%; RR = 2.08; 95% CI, 0.05-88.07; P = 0.70). In our study, phenylephrine-induced hypertension was associated with greater 90-day functional independence in selected AIS subtypes,without a significant increase in hemorrhagic transformation. However, the limited studies, predominance of retrospective designs, exclusively Asian cohorts, and heterogeneity in treatment protocols and populations warrant cautious interpretation and underscore the need for larger multicenter RCTs.

Introduction: AI-driven software has been recommended as a means of triaging suspected large vessel occlusions (LVOs) on CT angiography (CTA), and different solutions are now commercially available. This study compared the performance of two software packages, Brainomix 360 Triage LVO and Rapid LVO, in prospective, real-world use. Methods: Consecutive patients scanned for suspected stroke at 3 comprehensive stroke centres from May to July 2024 (3 months) were included in the study. CTA, where available, was processed in real time by both Rapid and Brainomix software. Ground truth on vessel occlusion was ascertained using clinical review of electronic medical records. Occlusion location was coded as intracranial internal carotid artery terminus (ICA-T), middle cerebral artery (MCA) M1 or MCA M2. If more than one occlusion was observed, the most proximal of these was taken as the primary vessel affected. Results: There were 561 suspected stroke patients scanned during the study period across the three sites. Of these, CTA was sent for processing by both Rapid LVO and Brainomix 360 Triage LVO for 359 cases. Cases were excluded if no contrast was detected (n=17), leaving 342 cases (Table 1). Of these, proximal LVO (ICA-T or MCA M1) was confirmed in 14 cases and MCA M2 occlusions were present in 6 cases. Table 2 shows the LVO detection results for all cases from the two software packages. For both devices, sensitivity was higher when considering only proximal occlusions (ICA-T or M1). Performance was similar overall, but there was a trend towards differing sensitivity for more distal occlusions. As a composite measure, area under the curve (AUC) was higher for Brainomix than Rapid (Figure 1) but this difference was not significant. The positive predictive value (PPV) was low for both software packages due to the low incidence of LVO in the patient population. Conclusions: This study demonstrated consistent performance of two AI software devices, Brainomix 360 Triage LVO and Rapid LVO, for detection of LVO on CTA imaging, in prospective, real-world use. The low number of positive cases in the dataset (N=21) is a limitation of the study. There was a trend towards different performance for more distal vessel occlusion locations. Overall, the sensitivity observed in this dataset was in line with reports from previous retrospective studies for both software packages and underlines the importance of prospective evaluation to understand real-world performance.

Introduction: Cerebral Amyloid Angiopathy (CAA) is caused by the progressive deposition of β-amyloid in the walls of small to medium-sized cerebral vessels. Although seizures represent a debilitating manifestation of CAA, little is known about their prevalence or associated factors. We aim to fill this gap by determining the prevalence of seizures in CAA and identify factors associated with an increased risk of seizures. Methods: We identified consecutive patients with CAA, evaluated within the Mayo Clinic health system between January 2010 and December 2023 using the ICD-10 code. Data on demographics and comorbidities were compared between those with and without seizures using the chi-square test for categorical variables and independent samples t-test for continuous variables. Odds ratios (OR) were estimated after adjusting for age, sex, and race in multivariable logistic regression. Results: We included 1,914 patients with CAA with a mean age of 75.4 ± 8.6 years, of whom 52.3% were female and 87.5% were white. Seizures were observed in 347 patients (18.1%). Individuals with seizures were significantly younger (mean age 67.7 ± 9.1 vs. 77.2 ± 7.5 years, p < 0.001). Multivariable modeling identified alcohol abuse (OR 1.92, 95% CI 1.03-3.56, p=0.04), diabetes mellitus (OR 2.14, 95% CI 1.13-4.06, p=0.02), hypertension (OR 1.79, 95% CI 1.33 - 2.41, p= <0.001), liver disease (OR 1.75, 95% CI 1.14-2.69, p=0.01), and ischemic stroke (OR 1.33, 95% CI 1.01-1.74, p=0.04) as independently associated with an increased risk of seizures. Conclusion: We found that seizures affect nearly one in five patients with CAA. Also, comorbidities such as alcohol abuse, diabetes mellitus, hypertension, liver disease, and ischemic stroke were found to be significantly associated with an increased risk of seizures. Longitudinal studies are warranted to confirm these risk factors, some of which may be modifiable.

Background: Artificial intelligence (AI) stroke imaging software is becoming mainstay in many hub-and-spoke hospitals. Brainomix 360 Stroke software is the market leader in Europe focused on leveraging simple imaging (non-contrast computerized topography (NCCT) and CT angiography (CTA)) and has recently been FDA-cleared for use in the USA. We evaluated the implementation of Brainomix 360 AI Stroke software in the 17 spoke, multi-state Mayo Clinic Health System (MCHS) telestroke network. Methods: This prospective study compared decision and treatment times before and after Brainomix AI implementation, as well as clinician feedback and simulated decision making to better understand any changes seen. Patients were included if they underwent telestroke evaluation at an MCHS emergency department within 90 days of implementation (2/9/2024-8/8/2024), and excluded if they were already admitted to the hospital or if video evaluation was not performed. Data collection included demographics, clinical decisions and treatment times. Qualitative surveys were conducted at baseline and evaluation end, as well as simulated decision making in 20 de-identified cases randomized to with/without AI through an online portal. Results: A total of 907 patients were included (444 pre- and 463 post-implementation, 287 (32%) with ischemic stroke final diagnosis). Median NIHSS was 2. IVT was recommended in 20.3% (27/148 ischemic stroke patients) pre- and 25.9% (36/139) post-implementation. EVT was recommended in 16.2% (24/148) pre and 14.4% (20/139) post. AI use was associated with trends of faster telestroke activation to IVT decision (36 vs 32 mins, p=0.6), IVT administration (47 vs 40 minutes, p=0.6), and EVT decision (36 vs 33 minutes, p=0.5). In the simulation, imaging interpretation was significantly faster when randomized to AI use (3.4 vs 2.1 mins, p<0.001) and associated with improved physician confidence (62 vs 88%, p=0.004). Qualitative feedback from 14/24 neurologists showed the greatest perceived value was improved confidence of treatment decisions (43%) and faster triaging (36%). Most value was gained from e-ASPECTS (NCCT) and LVO modules. Conclusion: This service evaluation highlighted the opportunity to improve efficiency of decision making even in the setting of a well-developed telestroke system. The impact was underpinned by the improved speed and confidence of imaging interpretation reflected in an offline simulation and qualitative feedback.

Introduction Telestroke enables timely and remote evaluation of patients with acute stroke syndromes. However, stroke mimics represent more than 30% of this population. Given the resources required for the management of suspected acute ischemic stroke, several scales have been developed to help identify stroke mimics. Our objective was to externally validate four mimic scales (Khan Score (KS), TeleStroke Mimic Score (TS), simplified FABS (sFABS), and FABS) in a large, academic telestroke network. Methods This is a retrospective, Institutional Review Board-exempt study of all patients who presented with suspected acute stroke syndromes and underwent video evaluation between 2019 and 2020 at a large academic telestroke network. Detailed chart review was conducted to extract both the variables needed to apply the mimic scales, the final diagnosis confirmed by final imaging, and discharge diagnosis (cerebral ischemic vs stroke mimic). Overall score performance was assessed by calculating the area under curve (AUC). Youden cutpoint was established for each scale and used to calculate sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy. Results A total of 1043 patients were included in the final analysis. Final diagnosis of cerebral ischemia was made in 63.5% of all patients, and stroke mimic was diagnosed in 381 patients (36.5%). To predict stroke mimic, TS had the highest AUC (68.3), sensitivity (99.2%), and NPV (77.3%); KS had the highest accuracy (67.5%); FABS had the highest specificity (55.1%), and PPV (72.5%). Conclusions While each scale offers unique strengths, none was able to identify stroke mimics effectively enough to confidently apply in clinical practice. There remains a need for significant clinical judgment to determine the likelihood of stroke mimic at presentation.

Introduction: Patients diagnosed with ischemic stroke routinely undergo prolonged cardiac telemetry (PCT) to evaluate for occult atrial fibrillation (AF), but this approach is limited by high cost and low yield. Clinical scores including CHA2DS2-VASc and CHARGE-AF can risk stratify patients but lack desired accuracy. Therefore, we aimed to evaluate an artificial intelligence (AI)-enabled ECG algorithm to predict AF detected by PCT after index stroke. Methods: This retrospective study included all adult patients with ischemic stroke evaluated at Mayo Clinic with baseline electrocardiogram (ECG) and PCT between 2018-2020. We recorded demographics, CHA2DS2-VASc score, CHARGE-AF score, stroke etiology, duration of PCT, and if PCT detected AF. ECGs were analyzed using the AI-ECG algorithm to determine AF likelihood. Stroke etiology was adjudicated using TOAST and embolic stroke of undetermined source (ESUS) definitions. The ability of the AI-ECG algorithm, CHA2DS2-VASc, and CHARGE-AF to predict AF detected by PCT was assessed via receiver operating characteristics analysis, calculating the area under curve (AUC) (c-statistic). Sensitivity and specificity analyses were performed for each tool using optimal cutoffs (using maximum Youden indices). Results: 863 patients were included; median age was 69 years, 496 (57.5%) were male, and 561 (65%) patients were categorized as ESUS. PCT detected AF in 85 (9.8%) patients. Median duration of cardiac telemetry was 30 (IQR 25-30) days. Sensitivity and specificity analyses are outlined in Table 1. The AI-ECG algorithm outperformed the CHA2DS2-VASc score (AUC 0.690 vs. 0.597, p=0.013), but was similar to the CHARGE-AF score (AUC 0.690 vs 0.651, p=0.233). Conclusions: Our AI-ECG algorithm demonstrated improved ability to predict AF detection on PCT after index stroke when compared to the CHA2DS2-VASc score, but was similar in performance to the CHARGE-AF score. All three methods had suboptimal accuracy.

Background and Objectives: It is unclear whether induced hypertension in acute ischemic stroke (AIS) may improve long-term outcomes. We conducted a systematic review and meta-analysis to compare the safety and efficacy of induced hypertension vs standard medical care in patients presenting with acute ischemic stroke within 72 hours of symptom onset. Methods: Two reviewers systematically searched PubMed using the prespecified terms “induced hypertension” or “blood pressure augmentation” plus “ischemic stroke” for clinical trials that reported functional independence (mRS 0-2) at 90 days. The safety outcome was symptomatic intracranial hemorrhage (sICH). Pooled odds ratio (OR) and 95% CI across trials were synthesized using fixed-effect model based on Mantel-Haenszel methods. Forest plots were constructed. Results: Three clinical trials with 270 AIS patients treated with induced hypertension (n=126) or standard medical care (n= 144) were included. Mean age was 66.6 years, 35.2% were women, and the mean initial NIHSS was 7.5. The rate of functional independence at 90 days was higher in patients treated with induced hypertension (70.6%) versus control (54.9%) (OR 1.88; 95% CI 1.13-3.11; p=0.01). sICH did not differ between patients treated with induced hypertension (<1%) versus control (<1%) (OR 1.03; 95% CI 0.14-7.45; p=0.98). Discussion: Among patients presenting with acute ischemic stroke with 72 hours of symptoms onset, induced hypertension resulted in better functional outcome at 90 days than standard medical care and did not increase risk of sICH. Large RCTs to test this observation are ongoing and needed to guide clinical practice.

Introduction: Recent studies report similar outcomes with antiplatelet drugs and intravenous thrombolysis (IVT) in patients with minor nondisabling ischemic stroke. These patients still meet emergent triage criteria and can increase the burden on stroke systems through prehospital stroke activation. Prehospital large vessel occlusion (LVO) scales can improve triage of LVO patients but have not been evaluated as tools to predict IVT administration. We sought to determine if common LVO scales can predict IVT use in a large academic telestroke practice. Methods: This retrospective study included all video telestroke consults performed in a large academic telestroke network from 2019-2020. Patients were excluded if they were on anticoagulation, presented beyond 4.5 hours from last known well, or family declined IVT due to goals of care. We recorded presenting NIHSS and IVT recommendations. NIHSS was used to calculate 7 LVO scales (RACE, G-FAST, FAST-ED, VAN, PASS, 3I-SS, and C-STAT). Scale performance was assessed by calculating area under curve (AUC) and compared to NIHSS total score using the DeLong test. Youden cutpoint was established for each scale and subsequently used to calculate sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy. Results: A total of 513 patients were included; 205/513 (40%) were recommended IVT. Median (IQR) NIHSS was 1 (0-3) in non-IVT patients and 6 (4-11) in IVT patients. The RACE and G-FAST scales were noninferior to the NIHSS in predicting IVT recommendation (Table 1). Conclusion: The RACE and G-FAST scales may be useful triage tools to identify patients needing emergent stroke evaluation for consideration of IVT.