Rabinstein, Alejandro A

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.

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.