Demaerschalk, Bart M.

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.

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Background The goal was to compare patterns of physical activity (PA) behaviors (sedentary behavior [SB], light PA, moderate‐to‐vigorous PA [MVPA], and sleep) measured via accelerometers for 7 days between patients with incident cerebrovascular disease (CeVD) (n=2141) and controls (n=73 938). Methods and Results In multivariate models, cases spent 3.7% less time in MVPA (incidence rate ratio [IRR], 0.963 [95% CI, 0.929–0.998]) and 1.0% more time in SB (IRR, 1.010 [95% CI, 1.001–1.018]). Between 12 and 24 months before diagnosis, cases spent more time in SB (IRR, 1.028 [95% CI, 1.001–1.057]). Within the year before diagnosis, cases spent less time in MVPA (IRR, 0.861 [95% CI, 0.771–0.964]). Although SB time was not associated with CeVD risk, MVPA time, both total min/d (hazard ratio [HR], 0.998 [95% CI, 0.997–0.999]) and guideline threshold adherence (≥150 min/wk) (HR, 0.909 [95% CI, 0.827–0.998]), was associated with decreased CeVD risk. Comorbid burden had a significant partial mediation effect on the relationship between MVPA and CeVD. Cases slept more during 12:00 to 17:59 hours (IRR, 1.091 [95% CI, 1.002–1.191]) but less during 0:00 to 5:59 hours (IRR, 0.984 [95% CI, 0.977–0.992]). No between‐group differences were significant at subgroup analysis. Conclusions Daily behavior patterns were significantly different in patients before CeVD. Although SB was not associated with CeVD risk, the association between MVPA and CeVD risk is partially mediated by comorbid burden. This study has implications for understanding observable behavior patterns in cerebrovascular dysfunction and may help in developing remote monitoring strategies to prevent or reduce cerebrovascular decline.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI’s role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI’s role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers’ effectiveness and well-being.

Background: As the United States medical system transitions to value-based reimbursement, interested parties will increase scrutiny in testing that may not alter management or outcomes. We investigated whether transesophageal echocardiography (TEE) provokes treatment alteration across age strata in hospitalized patients undergoing a TIA/stroke work-up. Methods: Consecutive patients admitted to the Mayo Clinic Arizona healthcare system between 1/1/2010 and 2/9/2015 were evaluated. A total of 832 patients were identified of which 314 were excluded primarily due to the fact that the TEE was performed as an outpatient. Therefore, 518 subjects (mean age = 68.2 +/- 14.1 y, 42.5% female) were included. A change in treatment was defined a priori as: anticoagulation (AC) initiated for atrial fibrillation found on TEE only (i.e. not identified on telemetry), complex proximal aortic arch plaque, left atrial or left atrial appendage thrombus, intracardiac mass, intracardiac shunt (atrial septal defect (ASD)/ patent foramen ovale (PFO)) ; PFO closure; cardiac mass removal; valve replacement for endocarditis; antibiotics for endocarditis. Results: See Tables A & B Conclusion: TEE in the evaluation of hospitalized patients with TIA/stroke resulted in a change in treatment in approximately 7% of patients regardless of age. Many of the decisions to change treatment were judgment-based and therefore adoption of the routine use of TEE in all patients with TIA/stroke is dependent on institutional practice standards.

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.

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 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, IRB exempt study of all patients who presented with suspected acute stroke syndromes and underwent video evaluation between 2019-2020 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 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 662/1043 patients (63.5%) and stroke mimic was diagnosed in 381/1043 patients (36.5%). To predict stroke mimic, TS had the highest AUC (68.3) and PPV (73%); KS had the highest accuracy (68.6%), sensitivity (86.6%) and NPV (61.5%); and FABS had the highest specificity (54.8%) (Table 1). Conclusions: While each scale offers unique strengths, none was able to identify stroke mimics effectively enough to confidently apply in clinical practice. There remains need for significant clinical judgment to determine the likelihood of stroke mimic at presentation.