Rabinstein, Alejandro A.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 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 Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Background and Purpose: Cerebral microbleeds (CMBs) are represented by small areas of hemosiderin deposition, detected on brain magnetic resonance imaging (MRI), and found in ≈23% of the cognitively normal population over age of 60 years. CMBs predict risk of hemorrhagic and ischemic stroke. They correlate with increased cardiovascular mortality. In this article, we sought to determine in a population-based study whether antithrombotic medications correlate with CMBs and, if present, whether the association was direct or mediated by another variable. Methods: The study consisted of 1253 participants from the population-based Mayo Clinic Study of Aging who underwent T2* gradient-recalled echo magnetic resonance imaging. We tested the relationship between antithrombotic medications and CMB presence and location, using multivariable logistic-regression models. Ordinal logistic models tested the relationship between antithrombotics and CMB frequency. Using structural equation models, we assessed the effect of antithrombotic medications on presence/absence of CMBs and count of CMBs in the CMB-positive group, after considering the effects of age, sex, vascular risk factors, amyloid load by positron emission tomography, and apoE. Results: Two hundred ninety-five participants (26.3%) had CMBs. Among 678 participants taking only antiplatelet medications, 185 (27.3%) had CMBs. Among 95 participants taking only an anticoagulant, 43 (45.3%) had CMBs. Among 44 participants taking an anticoagulant and antiplatelet therapy, 21 (48.8%) had CMBs. Anticoagulants correlated with the presence and frequency of CMBs, whereas antiplatelet agents were not. Structural equation models showed that predictors for presence/absence of CMBs included older age at magnetic resonance imaging, male sex, and anticoagulant use. Predictors of CMB count in the CMB-positive group were male sex and amyloid load. Conclusions: Anticoagulant use correlated with presence of CMBs in the general population. Amyloid positron emission tomography correlated with the count of CMBs in the CMB-positive group.

AbstractNeurological complications after heart transplantation are common and include cerebrovascular events (ischemic strokes, hemorrhagic strokes, and transient ischemic attacks), seizures, encephalopathy, central nervous system (CNS) infections, malignancies, and peripheral nervous system complications. Although most neurological complications are transient, strokes and CNS infections can result in high mortality and morbidity. Early recognition and timely management of these serious complications are crucial to improve survival and recovery. Diagnosing CNS infections can be challenging because their clinical presentation can be subtle in the setting of immunosuppression. Immunosuppressive medications themselves can cause a broad spectrum of neurological complications including seizures and posterior reversible encephalopathy syndrome. This article provides a review of the diagnosis and management of neurological complications after cardiac transplantation.

AbstractMyasthenia gravis and the associated pharmacologic management options could place patients at higher risk of contracting severe acute respiratory syndrome coronavirus 2 and exhibiting more severe manifestations of the novel coronavirus disease 2019 (COVID‐19). Multiple agents have been studied for the management of the COVID‐19, including remdesivir. To date, no published reports have evaluated the utilization of the antiviral remdesivir in patients with myasthenia gravis. We describe the first reported clinical course of three patients with myasthenia gravis who safely received remdesivir in combination with dexamethasone for the management of COVID‐19.

Background: The relationship between cerebral microbleeds (CMBs) on hemosiderin-sensitive MRI sequences and cerebral amyloid angiopathy (CAA) remains unclear in population-based participants or in individuals with dementia. Objective: To determine whether CMBs on antemortem MRI correlate with CAA. Methods: We reviewed 54 consecutive participants with antemortem T2*GRE-MRI sequences and subsequent autopsy. CMBs were quantified on MRIs closest to death. Autopsy CAA burden was quantified in each region including leptomeningeal/cortical and capillary CAA. By a clustering approach, we examined the relationship among CAA variables and performed principal component analysis (PCA) for dimension reduction to produce two scores from these 15 interrelated predictors. Hurdle models assessed relationships between principal components and lobar CMBs. Results: MRI-based CMBs appeared in 20/54 (37%). 10 participants had ≥2 lobar-only CMBs. The first two components of the PCA analysis of the CAA variables explained 74% variability. The first rotated component (RPC1) consisted of leptomeningeal and cortical CAA and the second rotated component of capillary CAA (RPC2). Both the leptomeningeal and cortical component and the capillary component correlated with lobar-only CMBs. The capillary CAA component outperformed the leptomeningeal and cortical CAA component in predicting lobar CMBs. Both capillary and the leptomeningeal/cortical components correlated with number of lobar CMBs. Conclusion: Capillary and leptomeningeal/cortical scores correlated with lobar CMBs on MRI but lobar CMBs were more closely associated with the capillary component. The capillary component correlated with APOE ɛ4, highlighting lobar CMBs as one aspect of CAA phenotypic diversity. More CMBs also increase the probability of underlying CAA.

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.