Citations

Abstract Background and aims Dementia represents a growing global health burden, with vascular dementia and Alzheimer's disease accounting for most cases through cumulative cerebrovascular damage. While periodontal disease (PD) is associated with cognitive decline, dental caries represents a distinct oral infection pathway characterized by bacterial invasion and infection, potentially contributing to vascular brain injury through microbial translocation, endothelial dysfunction, systemic inflammation, and cerebral microvascular damage. The relationship between dental caries and incident dementia within a vascular framework remains understudied. Methods We analyzed 6,542 participants from the Atherosclerosis Risk in Communities (ARIC) Study who received dental examinations at Visit 4 (1996–1998) and followed for incident dementia through 2023 using standardized surveillance (Figure-1). Cox proportional hazards models with competing risk accounted for death. Multivariable models adjusted for demographic factors, cardiovascular risk factors, BMI, physical activity, education, income, insurance status, APOE4 genotype, PD, and regular dental care. Results Mean age was 62.4 years; 54% female, 82% White, and 18% Black. Dental caries was present in 20%. Over median 25-year follow-up, individuals with dental caries demonstrated significantly higher cumulative dementia incidence (Gray’s test p<0.0001). After full adjustment, dental caries remained independently associated with increased dementia risk (adjusted HR 1.20, 95% CI 1.02–1.42), independent of PD and APOE4 status (Figure-2). Conclusions Dental caries was independently associated with increased incident dementia risk. These findings support a potential role for caries-related oral infection in vascular brain injury pathways relevant to vascular dementia and stroke-related cognitive impairment. Oral infection prevention may represent a modifiable target within cerebrovascular disease prevention strategies. Conflict of interest Alana E. Barton, Souvik Sen, Kevin Moss, James Selph, Ryan Demmer, Pamela L. Lutsey, Priya Palta, Kamakshi Lakshminarayan, Karly Pikel, Wayne Rosamond: nothing to disclose Figure 1 - belongs to Conclusions Figure 2 - belongs to Conclusions

Abstract Background and aims Systemic inflammation has been implicated in cognitive decline and dementia pathogenesis. Interleukin-6 (IL-6), a pro-inflammatory cytokine associated with cardiovascular disease, may play a role in cerebrovascular and neurodegenerative processes. We examined the relationship between IL-6 levels and incident dementia risk using the Atherosclerosis Risk In Communities (ARIC) study. Methods ARIC study participants at Visit 4 (1996-1998) had IL-6 measurements and were followed for incident dementia over 25 years. IL-6 levels were dichotomized at the median value of 1.5 pg/mL. Incident dementia cases were identified through standardized surveillance. Cox proportional hazards regression calculated hazard ratios (HR) with 95% confidence intervals (CI), adjusting for age, race, gender, hypertension, diabetes, obesity, physical activity, smoking, alcohol use, education, income, insurance status, and ApoE-ε4 allele status. Results Participants (n=5,662; mean age 62.4±5.6 years, 55% female, 83% White, 17% Black) were followed for a median of 25 years. Those with IL-6 >1.5 pg/mL demonstrated significantly increased dementia risk versus IL-6 ≤1.5 pg/mL. The crude HR was 1.23 (95% CI 1.11-1.37). After adjustment, the association remained significant with HR 1.13 (95% CI 1.01-1.27). Kaplan-Meier survival curves showed progressive divergence between groups, with lower dementia-free survival in the higher IL-6 group, log-rank p<0.001. Conclusions Elevated mid-life IL-6 levels (>1.5 pg/mL) independently predict increased long-term dementia risk over 25 years, even after adjusting for cardiovascular and sociodemographic confounders. These findings support systemic inflammation's role in dementia pathogenesis and highlight IL-6 as a potential biomarker for dementia risk. Conflict of interest Karly Pikel, Natasha Carmichael, Alana E. Barton, Kevin Moss, Christine Downey, Ryan T. Demmer, Pamela L. Lutsey, Souvik Sen: Nothing to disclose Figure 1 - belongs to Conclusions

Abstract Background and aims BotulinumtoxinA (BoNT-A) is a guideline-recommended treatment for spasticity following stroke. We evaluated BoNT-A treatment persistence drivers of discontinuation and predictors of non-persistence in patients with stroke. Methods Eligible adults had received ≥1 treatment cycle with BoNT-A at the Mayo Clinic (January 2010–June 2023) and had an International Classification of Diseases (ICD9/10) code for stroke. Patients with ICD9/10 codes for migraine, cerebral palsy, cervical dystonia, or bladder dysfunction were excluded. Index date (baseline) was the date of first BoNT-A treatment; follow-up was 2 years post-index. Patients were persistent if all treatment intervals post-index were ≤the 80th percentile of all patients. Reasons for discontinuation were identified by AI natural language processing analysis of physicians’ free-text notes. Baseline characteristics associated with non-persistence were identified using step-wise logistic regression analyses. Results Overall, 407 patients were included. The 80th percentile of the BoNT-A treatment interval was 154 days; 5.7% of patients were persistent. Mean (95%CI) number of BoNT-A treatments over the 2-year follow-up was 2.43 (2.24–2.63) cycles. Comorbidity burden was high (Table 1). The most common reasons for BoNT-A discontinuation were patient- and physician-reported lack of treatment effectiveness (Table 2). Older age, Hispanic/Latino/unknown/undisclosed ethnicity, presence of comorbidities, and use of medications of interest at baseline (skeletal muscle relaxants, antidepressants, anticonvulsants, opiates, sedatives/hypnotics, NSAIDs) were significantly associated with non-persistence (Table 3). Conclusions Over 2 years, BoNT-A treatment persistence was low. Lack of treatment effectiveness was the most common reason for discontinuation. Addressing drivers of non-persistence may help improve BoNT-A treatment outcomes. Conflict of interest Funding: Ipsen. Disclosure of Conflicts of Interest: Michael Wainberg: has received consulting fees from Ipsen. Billie Schultz: has received consulting fees from Ipsen. Jinming Zhang, Simon Page, Jonathan Bouchard, Amandeep Mann, Mathieu Beneteau are employees of Ipsen. Table 1 - belongs to Conclusions Table 2 - belongs to Conclusions Table 3 - belongs to Conclusions

Abstract Background and aims Cardiac CT is increasingly used in the acute diagnostic work-up of acute ischemic stroke (AIS), but commonly used scan protocols do not allow assessment of left ventricular ejection fraction (LVEF), a known predictor of functional outcome. We aimed to develop a prediction model for reduced LVEF using clinical and imaging data. Methods This post-hoc analysis of the Mind the Heart study included AIS patients who underwent acute cardiac CT (acquired during diastole) and routine diagnostic work-up, including transthoracic echocardiography (TTE), which served as the reference standard for LVEF. Multivariable logistic regression models incorporating baseline variables, arterial input function (AIF) derived from CT-perfusion, and left atrial and ventricular cardiac CT volumes were developed using the training set (80%) to predict reduced LVEF (<50%). Performance was evaluated on the testing set (20%) using receiver operating characteristic analysis, reporting the area under the curve (AUC), sensitivity, and specificity. Non-linear associations were modeled using restricted cubic splines, and Least Absolute Shrinkage and Selection Operator regression was used for variable selection. We evaluated whether cardiac CT information improved model fit. Results We included 298 AIS patients (61% male, median age 71 [IQR 61-79] years), of whom 87 (29%) had reduced LVEF. The final model showed moderate discrimination (AUC 0.71), sensitivity (0.67) and specificity (0.70). Including cardiac CT volumes in addition to baseline and AIF variables improved model fit (p=0.04). Conclusions Our model may aid risk stratification for reduced LVEF in AIS patients undergoing acute cardiac CT and could support identification of patients in whom additional TTE is warranted. Conflict of interest MMW received the Arthur Fonville Award for Stroke Research in 2025 for this work. SSN received travel grants from the Cultuurfondsbeurs, Remmert Adriaan Laan Fonds, CONTRAST consortium and Dr. Jan Meerwaldt Stichting outside the submitted work, JMC reports grants from Medtronic, Siemens, AstraZeneca and Bayer outside the submitted work (all paid to institution). JMC is shareholder and co-founder of TrianecT. CBM reports grants from Stryker and Boeringher-Ingelheim outside the submitted work (paid to institution) and is shareholder of Nicolab (minority interest). LAR was supported by a personal Dekker Junior Clinical Scientist Grant from the Dutch Heart Foundation.The other authors have no financial conflicts of interest.

Abstract Aims To compare the prevalence of autism spectrum disorder (ASD) in children with and without epilepsy using clinical diagnoses and chart review with research‐defined criteria and to examine sex ratios, age at ASD onset, and intellectual disability between groups. Method The Olmsted County birth cohort included 30 490 individuals, of whom 257 (0.84%) had epilepsy diagnosed before 19 years of age. The cohort was previously screened for ASD using three case definitions: research‐inclusive (ASD‐R I ), research‐narrow (ASD‐R N ), and clinically diagnosed ASD (ASD‐C). For those with ASD‐R I , we assessed sex, intellectual disability (IQ < 70), and age at ASD onset. Means and proportions are reported when available. Results ASD prevalence was significantly higher in participants with epilepsy across all definitions: ASD‐R I 21.4% versus 3.2%; ASD‐R N 14.0% versus 1.6%; and ASD‐C 7.9% versus 0.7%. Children with both epilepsy and ASD‐R I were more often female (38.2% vs 25.8%), had higher rates of intellectual disability (56.5% vs 15.4%), and met the ASD‐R I criteria at a younger age (mean 7 years 5 months vs 8 years 8 months). Interpretation Co‐occurring ASD and epilepsy are frequently associated with intellectual disability, earlier age at ASD diagnosis, and reduced male predominance.

Abstract Background and aims Interfacility transfers in rural areas are challenging due to limited ambulance and bed availability, image sharing issues, and access disparities. Regional transfers to strategically located primary stroke centers can reduce time to care, decrease helicopter use, support local economies, and conserve comprehensive stroke center (CSC) resources. This study established a primary stroke center as a regional transfer center (rSTC) within a telestroke network. Methods Over one year, stakeholders created regionally appropriate transfer workflows defined as patients without large vessel occlusion, low likelihood for neurosurgical decompression or critical illness within a rSTC and 4 surrounding centers. Continuous education and prospective case logs were maintained by the rSTC stroke coordinator and a retrospective chart review was conducted one year following the intervention period. Results Of 99 patients requiring transfer, 73 (73.7%) went to the rSTC, with an average stay of 2.1 days, no deaths, and no secondary CSC transfers. The remaining 26 (26.3%) were sent to the CSC, with longer stays (10.6 days) and higher mortality (23.1%). Transfers to the rSTC were faster (60 min vs. 188 min). Financial analysis showed significant EMS savings ($455,111) and local health system benefits ($730,000) when compared to the pre-intervention period. Conclusions Establishing an effective regional transfer model is possible with broad stakeholder engagement and regular network maintenance. rSTCs provide the right level of care for the right patient, improving efficient allocation of healthcare resources and avoiding futile transfers with costly admissions far from home while opening beds and reducing transfer times to CSCs. Conflict of interest Nothing to disclose

Abstract Background and aims Rural communities have a high incidence of stroke driven by high prevalence of risk factors. We hypothesize that patient centric stroke prevention education can be delivered in rural communities. Methods High risk patients defined as Atherosclerotic Cardiovascular Disease (ASCVD) 10-year risk of ≥ 20% cardio-cerebrovascular events were identified through electronic medical records receiving primary care in Greenville and Lakeview (intervention sites) and Muskegon (control site) were included. Education material included lifestyle modification and stroke signs-symptoms information. Patients completed a pre-intervention survey, recieved standardized stroke education, and were assessed for knowledge retention through quarterly telephone follow-ups and completed a 12-month post-intervention survey. Total study cost is 130,000$ over 18 months. Results High ASCVD score was identified in 758 (Intervention) and 1128 (Control) patients. 70 intervention and 73 control patients were enrolled. Time spent consenting patients and delivering education is 5 minutes with additional 10-15 minutes at each 3-month follow-up call. 65 intervention patients completed 12 month follow up. Intervention vs. Control; Median: IQR: Age [74 (68-76) vs. 73(68-75) years; p=0.20]; Initial ASCVD [24.5 (22-30) vs. 28.6 (26-37); p&lt;0.001], 12-month ASCVD [25(21-30) vs. 30(24-39); p=0.002). Post-intervention survey showed significant improvement in recognition of stroke risk factors (p&lt;0.001) and stroke signs-symptoms (p&lt;0.001). 49 (75%) patients adopted healthy lifestyle at 12-month follow up. Conclusions A primary stroke prevention cost-effective education program can be successfully implemented in a high-risk rural population, demonstrating improved stroke literacy and healthy lifestyle adoption. Conflict of interest

Background: Several special tests for detecting medial ulnar collateral ligament (MUCL) injuries of the elbow have been described, but data on the clinical utility of these tests are limited. Purpose: To determine the clinical utility of selected elbow special tests to detect MUCL injuries in an athletic population. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 84 consecutive patients with sports-related elbow pain underwent a standardized clinical examination. Overall, 6 special tests (moving valgus stress test, static valgus stress test, modified valgus stress test, milking maneuver, pronation flexion resistance test, and temple/cheek press test) were performed. Sensitivity, specificity, accuracy, positive/negative predictive value, and positive/negative likelihood ratio (LR+ and LR–, respectively) were calculated for individual tests and combinations of tests to detect MUCL injuries. Higher values for the LR+ and lower values for the LR– represent stronger clinical utility. Results: For detecting MUCL injuries, the moving valgus stress test resulted in the highest sensitivity (98%) and accuracy (93%) with the lowest LR– (2%). The milking maneuver had the highest LR+ (5.1) with the second highest sensitivity (77%) and accuracy (79%). The pronation flexion resistance test and the temple/cheek press test had the lowest LR+ (1.5 and 1.8, respectively) and highest LR– (94% and 95%, respectively) in addition to the lowest sensitivity (16% and 9%, respectively) and accuracy (33% and 30%, respectively). No combination of tests resulted in increased clinical utility. Conclusion: The moving valgus stress test and milking maneuver demonstrated acceptable clinical utility in which the moving valgus stress test was better for ruling out MUCL injuries and the milking maneuver was better at ruling in MUCL injuries. There was no added value when combining the 2 tests together or with other maneuvers.

ABSTRACT Clinical worsening events are increasingly recognized as a meaningful outcome in pulmonary arterial hypertension (PAH). We applied machine‐learning models to real‐world data to identify clinical features that may predict clinical worsening events in PAH. Data were obtained retrospectively from the electronic health records of adults diagnosed with PAH at Mayo Clinic locations (January 2015–December 2019). Machine‐learning LASSO regularized logistic regression models were developed to analyze the association between 100 clinical features and occurrence of clinical worsening events. In total, 455 patients were included (mean age 62.1 years; 59.3% female). Of these, 232 (51.0%) experienced a clinical worsening event after a median (quartile 1, quartile 3) of 10.9 (4.1, 21.4) months. The best‐performing model had an area under the curve of 0.78, sensitivity of 78%, and specificity of 59%. The model identified 11 clinical features associated with clinical worsening events in PAH with a non‐zero coefficient: baseline age, body mass index (BMI), creatinine level, red cell (erythrocyte) distribution width (RDW), mean pulmonary arterial pressure, pulmonary vascular resistance, sodium concentration, and QT interval, along with dyspnea, number of clinic visits, and other forms of heart disease within a 1‐month window before a clinical worsening event. Baseline RDW and BMI, and number of clinic visits within the 1‐month window were statistically significantly associated with clinical worsening events. Our study used machine‐learning models to identify clinical features associated with risk of clinical worsening events in PAH. Automation of risk prediction could lead to earlier therapeutic intervention to optimize patient outcomes.