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Background: Atrial fibrillation (AF) is an established risk factor for ischemic stroke, but it can be paroxysmal and may go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently shown to detect silent AF. The objective of this study was to determine if AI-ECG AF score is associated with presence of cerebral infarcts. Methods: Participants from a population-based study ages 30 to 95 years with T2 fluid attenuation inversion recovery (FLAIR) MRI obtained between October 10, 2011, and November 2, 2017 were considered for inclusion. Participants without ECG were excluded. AI-ECG score was calculated using most recent ECG with normal sinus rhythm at the time of MRI. Presence of infarcts was determined on FLAIR MRI scans. Logistic regression was run to evaluate the relationship between AI-ECG AF score and presence of cerebral infarcts. Similar analyses were performed using history of AF rather than AI-ECG AF score as predictor. Age and sex were included as covariates. We also examined whether a high-threshold AI-ECG score was associated with infarcts. In a prior study, an AI-ECG AF score > 0.5 was associated with a cumulative incidence of AF of 21.5% at 2 years and 52.2% at 10 years. Results: This study included 1,373 individuals. Average age was 69.6 years and 53% of participants were male. There were 136 (10%) individuals with ECG-confirmed AF; 1237 (90%) participants had no AF history. Of participants with AF, 23% (n=31) were on anticoagulation, 47% (n=64) were on antiplatelet and 18% (n=24) were on dual therapy. Only 1.3% (n=16) of patients without AF were on anticoagulation and 47% (n=578) were on antiplatelet therapy. Ischemic infarcts were detected in 214 (15.6%) patients. As a continuous measure AI-ECG was associated with infarcts but not after adjusting for age and sex (p=0.46). AI-ECG AF score > 0.5 was associated with infarcts ( p < 0.001); even after adjusting for age and sex ( p = 0.03). History of AF was also associated with infarcts after adjusting for age and sex ( p = 0.018). Conclusion: AI-ECG AF score and history of AF were associated with presence of cerebral infarcts after adjusting for age and sex. This tool could be useful in select patients with cryptogenic stroke but further investigation would be required.

Introduction: Patient selection for acute stroke revascularisation therapies is commonly based on clinical-imaging mismatch paradigms. Anatomical scores or total volumes of brain affected are assumed to correlate with clinical deficit without reference to the functional eloquence of the regions involved. In this study we used the relationship between presenting hypoperfusion abnormality and the corresponding NIHSS to generate functionally weighted atlases from patients with acute ischaemic stroke. Methods: Patients with acute large vessel occlusion (LVO) of the anterior circulation and with CTP at the time of presentation were included in this study. CTP was analysed with e-CTP (Brainomix Ltd., Oxford, UK) and hypoperfusion maps defined (Tmax >6s). Voxelwise lesion to symptom mapping was used to generate probability maps associating regions of hypoperfusion with clinical deficit at presentation. Results: 58 patients with itemised NIHSS and CTP were included. Discrete patterns of deficit were associated with each NIHSS domain. Total NIHSS correlated most strongly with left MCA involvement, consistent with the weighting towards language in this clinical scale. Regions associated with upper and lower limb deficits identified the respective motor cortex regions and descending motor tracts. Example mapping is presented in the Figure (A: total NIHSS, B: left arm paresis, C: left leg paresis). Data from a larger cohort will be presented at the meeting. Conclusions: Functional mapping using associations between presenting NIHSS and hypoperfusion lesion can be used to create eloquence-weighted atlases. Such atlases from stroke patients may be helpful to develop tools to predict clinical deficit associated with a presenting ischemic core lesion defined on non-contrast CT or CTP. This approach could refine clinical-imaging mismatch concepts, expanding endovascular treatment to a wider group of patients who would not currently be treated.

Background and Purpose: The diagnosis of spontaneous spinal cord infarction (SCI) is limited by the lack of diagnostic biomarkers and MRI features that often overlap with those of other myelopathies, especially acute myelitis. We investigated whether the ratio between serum neurofilament light chain levels and MRI T2-lesion area (neurofilament light chain/area ratio—NAR) differentiates SCI from acute myelitis of similar severity. Methods: We retrospectively identified Mayo Clinic patients (January 1, 2000–December 31, 2019) with (1) SCI, (2) AQP4 (aquaporin 4)-IgG or MOG (myelin oligodendrocyte glycoprotein)-IgG-associated myelitis at disease clinical presentation, or (3) idiopathic transverse myelitis from a previously identified population-based cohort of patients seronegative for AQP4-IgG and MOG-IgG. Serum neurofilament light chain levels (pg/mL) were assessed at the Verona University (SIMOA, Quanterix) in a blinded fashion on available stored samples obtained ≤3 months from myelopathy presentation. For each patient, the largest spinal cord lesion area (mm 2 ) was manually outlined by 2 independent raters on sagittal T2-weighted MRI images, and the mean value was used to determine NAR (pg/[mL·mm 2 ]). Results: Forty-eight patients were included SCI, 20 (definite, 11; probable, 6; possible, 3); acute myelitis, 28 (AQP4-IgG-associated, 17; MOG-IgG-associated, 5; idiopathic transverse myelitis, 6). The median expanded disability status scale score (range) at myelopathy nadir were 7.75 (2–8.5) and 5.5 (2–8), respectively. Serum neurofilament light chain levels (median [range] pg/mL) in patients with SCI (188 [14.3–2793.4]) were significantly higher compared with patients with AQP4-IgG-associated myelitis (37 [0.8–6942.9]), MOG-IgG-associated myelitis (45.8 [4–283.8]), and idiopathic transverse myelitis (15.6 [0.9–217.8]); P =0.01. NAR showed the highest accuracy for identification of SCI versus acute myelitis with values ≥0.35 pg/(mL·mm 2 ) yielding 86% specificity and 95% sensitivity (area under the curve=0.93). The positive and negative likelihood ratios were 6.67 and 0.06, respectively. NAR remained independently associated with SCI after adjusting for age, gender, immunotherapy before sampling, and days from myelopathy symptoms onset to sampling ( P =0.0007). Conclusions: NAR is a novel and promising clinical biomarker for differentiation of SCI from acute myelitis.

Background There are limited contemporary data prevalence and outcomes of acute ischemic stroke (AIS) complicating acute myocardial infarction (AMI). Methods and Results Adult (>18 years) AMI admissions using the National Inpatient Sample database (2000–2017) were evaluated for in‐hospital AIS. Outcomes of interest included in‐hospital mortality, hospitalization costs, length of stay, discharge disposition, and use of tracheostomy and percutaneous endoscopic gastrostomy. The discharge destination was used to classify survivors into good and poor outcomes. Of a total 11 622 528 AMI admissions, 183 896 (1.6%) had concomitant AIS. As compared with 2000, in 2017, AIS rates increased slightly among ST‐segment–elevation AMI (adjusted odds ratio, 1.10 [95% CI, 1.04–1.15]) and decreased in non–ST‐segment–elevation AMI (adjusted odds ratio, 0.47 [95% CI, 0.46–0.49]) admissions ( P <0.001). Compared with those without, the AIS cohort was on average older, female, of non‐White race, with greater comorbidities, and higher rates of arrhythmias. The AMI‐AIS admissions received less frequent coronary angiography (46.9% versus 63.8%) and percutaneous coronary intervention (22.7% versus 41.8%) ( P <0.001). The AIS cohort had higher in‐hospital mortality (16.4% versus 6.0%; adjusted odds ratio, 1.75 [95% CI, 1.72–1.78]; P <0.001), longer hospital length of stay, higher hospitalization costs, greater use of tracheostomy and percutaneous endoscopic gastrostomy, and less frequent discharges to home (all P <0.001). Among AMI‐AIS survivors (N=153 318), 57.3% had a poor functional outcome at discharge with relatively stable temporal trends. Conclusions AIS is associated with significantly higher in‐hospital mortality and poor functional outcomes in AMI admissions.

Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

Abstract Background SMART (stroke‐like migraine attacks after radiation therapy) is a rare, delayed complication of brain radiation. In this study, we wanted to review the spectrum of symptoms, neuroradiological findings, autoimmune status, and outcomes in SMART syndrome patients. Methods We conducted a retrospective cohort study of all consecutive adult patients (≥18 years) diagnosed with SMART syndrome at Mayo Clinic, Rochester between January 1995 and December 2018. Results We identified 25 unique patients with SMART syndrome and a total of 31 episodes and 15 (60%) patients were male. The median age at onset was 46 (interquartile range [IQR] 43–55) years and the median latency of onset after the initial radiation was 21.6 (IQR 14.4–28.2) years. Magnetic resonance imaging (MRI) showed gyral edema and enhancement in all cases with the temporal (25, 80.6%) and parietal (23, 74.2%) lobes being the most commonly affected. The median follow‐up of the patients in our cohort was 10 (IQR 6–32) weeks. On univariate analysis, factors associated with an increased risk of recurrent SMART episodes were female gender (odds ratio [OR] 8.1, 95% confidence interval [95% CI] 1.1–52.6, p = 0.019) and absence of electrographic seizure discharges during initial symptoms (OR 7.4, 95% CI 1.1–45.9, p = 0.032). We could not identify an autoimmune etiology. Longer duration of symptoms (>10 weeks) correlated with an older age ( p = 0.049), temporal lobe involvement ( p < 0.001), and diffusion restriction ( p = 0.043). Conclusions SMART is a syndrome with characteristic imaging findings and clinical features. Incomplete recovery by 10 weeks occurred in one‐third of individuals and was associated with older age, temporal lobe involvement, and restricted diffusion on MRI.