Demaerschalk, Bart M.

e23345 Background: Telemedicine enables patients to attend visits remotely and is associated with shorter wait times (i.e. scheduling-to-appointment duration). However, its impact on patient–clinic distance and wait times for new patients in high-volume surgical oncology centers remains unclear. In these centers, where appointment availability and travel distance significantly influence care decisions, understanding telemedicine’s impact on access is essential as federal policies supporting telemedicine continue to be under legislative review. To address this, we examined the relationship between new patient visit modality, distance, and wait time across demographics, providing insights to guide policy makers, and clinical practice. Methods: We extracted visit data from a multispecialty surgical oncology center in Northern California from 2019 to 2023 and used linear models with fixed effects for clinician and patient characteristics to assess the association of NPV modality with distance and wait times. We calculated distance using geodesic measurements between residential ZIP codes and care sites and derived median household income and education levels from the American Community Survey. Results: There were 10596 NPVs in 2019, and 13982 in 2023, conducted by 78 and 100 clinicians respectively. The proportion of NPV visits conducted by telemedicine increased from 0% to 28% during this period. From 2021–2023, NPVs conducted by telemedicine were more likely to fall outside the center’s 2019 catchment area (80th percentile of distance) with an adjusted odds ratio of 1.89 (95% CI 1.62, 2.21). Wait times for telemedicine NPVs was 3.0 days shorter (95% CI [2.0, 3.9]) than in-person visits. Compared to the reduction in wait times for patients aged 45–65 (2.8 days, 95% CI [1.8, 3.8]), those 80 and older had a smaller reduction (1.9 days, 95% CI [0.9, 2.8]), while patients aged 65–79 had a greater reduction (3.4 days, 95% CI [2.4, 4.5]). This effect did not significantly vary by sex, race, ethnicity, education, income, or insurance status. Conclusions: At this multispecialty surgical oncology center, new patients seen via telemedicine were more likely to reside outside the center’s traditional catchment area and had shorter wait times, with these differences varying by demographic characteristics. Strategic telemedicine integration tailored to patient needs could enhance access by reducing wait times and expanding specialized surgical oncology care to patients across broader geographic distances.

To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least one prior stroke. Eligible participants (n = 1186) completed an MRI scan. Depression was classified based on positive depression screening scores (PHQ-2 ≥ 3). Multivariate linear regression models assessed the relationships between depression and structural and diffusion measures generated from brain MRI scans. Logistic regression models were used to examine the relationship between accelerometer-measured daily PA and future depression (n = 367). Depression was positively associated with total white matter hyperintensities (WMHs) volume (standardized β [95% CI]—0.1339 [0.012, 0.256]; FDR-adjusted p-value—0.039), periventricular WMHs volume (standardized β [95% CI]—0.1351 [0.020, 0.250]; FDR-adjusted p-value—0.027), and reduced MD for commissural fibers (standardized β [95% CI]—−0.139 [−0.255, −0.024]; adjusted p-value—0.045). The odds of depression decreased by 0.3% for each daily minute spent in objectively measured light PA, while each minute spent in sleep from midnight to 6:00 AM was associated with a 0.9% decrease in the odds of depression. This early-stage analysis using a population cohort offers a scientific rationale for researchers using multimodal data sources to investigate the heterogenous nature of PSD and, potentially, identify stroke patients at risk of poor outcomes.

Introduction: Informed consent (IC) plays a central role in medical care and research. Practices for endovascular thrombectomy (EVT) in acute stroke are not well elucidated. We investigated the clinical roles and medical specialties of individuals who obtain IC for EVT, aiming to provide insights for enhancing the process in contemporary stroke care. Methods: We conducted a survey from July to December 2023 among clinicians involved in acute stroke care. Utilizing Qualtrics, we disseminated a structured questionnaire through various national and international platforms including the American Academy of Neurology and StrokeNet. This analysis summarizes findings pertaining to the characteristics of those who obtain EVT IC at the respondents’ institution. Results: Among 168 participants, 71% were staff physicians, 70% practiced in the United States (US), and 70% were based at academic centers. The most common medical specialties obtaining EVT IC were neurology (77%), followed by neurosurgery (41%), radiology (30%) and emergency medicine (10%) ( Table 1 ). Staff physician (61%) was the most common clinical role involved, followed by fellows (43%), residents (48%), and advanced practice providers (APP, 36%) ( Table 2 ). Non-US institutions were more likely to utilize a provider from neurology alone (50% vs. 31%, p=0.016) and staff physicians (76% vs. 54%, p=0.008), while US institutions were more likely to utilize providers from neurosurgery (51% vs. 18%, p<0.001), APPs (43 vs. 18%, p=0.002) and residents (56% vs. 28%, p=0.001). Non-academic institutions more frequently used emergency medicine providers (25% vs. 5%, p<0.001) and APPs (50% vs. 31%, p=0.031), while academic institutions commonly utilized neurosurgery providers (48% vs. 18%, p=0.001), residents (59% vs. 13%, p<0.001) and fellows (52% vs. 18%, p<0.001). Conclusion: This study highlights the diverse medical specialties and clinical roles of persons obtaining IC for EVT, with neurologists and staff physicians being the most frequently utilized. These variations likely reflect availability and capacity of certain providers in different settings. Future efforts to optimize the IC process should be multi-disciplinary, with standardized content that addresses issues such as ischemic core volume, outcome expectations and diversity.

Introduction: Previous studies have established the safety and economic benefits of outpatient Transient Ischemic Attack (TIA) management, highlighting reduced expenses for outpatient evaluation. This context underscores the potential advantages of refined outpatient TIA management, a focus of our study, through the implementation of a novel expedited outpatient TIA workflow and assessment of its economic, administrative and clinical impact. Methods: We implemented a novel TIA pathway including streamlined assessments by emergency department (ED) physicians, a structured expedited set of diagnostic tests and outpatient follow-up with vascular neurologists within 48-72 hours of ED presentation. We conducted a retrospective cost-efficacy analysis, contrasting two phases: pre-implementation (standard ED-to-hospital admission TIA workflow from May 1, 2020 - December 31, 2020) and post-implementation (ED-to- outpatient TIA workflow from May 1, 2021 - December 31, 2021). We also compared the clinical outcomes of patients with a final diagnosis of TIA or minor ischemic stroke syndrome from the implementation cohort (May 2021-May 2022) with those from an ABCD2-matched inpatient TIA cohort evaluated in the pre-implementation period (2017-2021). Results: The newly implemented outpatient TIA workflow resulted in a 7% decrease in admissions to hospital observation status, 14.8% reduction in hospital admissions, whereas ED discharge rates rose by 20.4%. Within 8 months, these changes equated to 21 fewer hospitalizations or admissions, reducing costs by approximately $103,320 (Figure1). Laboratories, diagnostic imaging, and neurology evaluations were completed faster in the inpatient pathway. Both pathways had similar vascular risk factors and outcomes regarding final diagnosis, - diagnostic testing achieved, preventative treatments prescribed, stroke/TIA recurrences, re-admissions, TIA-related morbidity and mortality, and related ED return visits (Table 1). Conclusion: The deployment of an optimized outpatient TIA management workflow at Mayo Clinic Arizona had demonstrated improved administrative and cost efficiency, with similar clinical outcomes as inpatient TIA pathway. This investigation corroborates the efficacy of workflow enhancements in outpatient TIA settings and suggests a scalable model for analogous strategies in other medical centers.

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.

Background: Although no tests are currently available to predict post-stroke depression (PSD), a risk factor for diminished quality of life and stroke-related death, emerging evidence suggests that ischemic stroke (IS) may induce behavioral changes by damaging connections throughout the brain rather than via localized injury. Hypothesis: We sought to determine if active (surveys) and passive (accelerometer, GPS, and other sensors) smartphone data could quantify exploratory behavioral proxies of PSD. Methods: Thirty-four adults with a recent IS (n = 12) or transient ischemic attack (TIA) (n = 22) diagnosis were followed for 3 months. Participants used the Beiwe app to complete a weekly Patient Health Questionnaire-8 (PHQ-8) survey and were evaluated every 30 days with the Montgomery-Asberg Depression Rating Scale (MADRS). Results: Preliminary analysis of app survey compliance was adequate (86%), and the quantity of data collected was greater for Android than iOS phones. PHQ-8 indicated depression (moderate or severe) in 20% of participants. MADRS highly correlated with the most recent PHQ-8 ( r = 0.92, 95% CI (0.19, 0.99), P = 0.028). Several exploratory passive physical activity and social measures showed statistically significant change over time and/or associations with PHQ-8 and MADRS. Conclusion: Low-burden smartphone data holds promise for developing novel IS and TIA outcome measures as well as identifying patients most at risk of PSD.