Citations

Background: Whether a significant proportion of cryptogenic stroke (CS) is caused by under-detected paroxysmal atrial fibrillation (PAF) is not known. Prolonged ambulatory cardiac monitoring may detect episodes of PAF in patients with stroke of unknown cause (cryptogenic stroke, CS). However, it is unclear whether PAF is more common in patients wtih CS compared with patients with stroke of known cause (SKC). We performed a case control study to compare the rates of detection of PAF in CS vs SKC using prolonged ambulatory cardiac rhythm monitoring. Methods and Results: We prospectively enrolled 132 patients within 3 months of a stroke (66 with CS and 66 with SKC) to undergo 3-week ambulatory cardiac rhythm monitoring using the CardioNet MCOT system. Stroke group was assigned by consensus between two vascular neurologists upon enrollment based on the Causative Classification of Stroke system. Primary endpoint was detection of PAF of any duration independently confirmed by two cardiologists blinded to the stroke group. The groups were well balanced except that patients with CS tended to be older and to have less hypertension and diabetes. Four patients (2 in each group) were excluded because of insufficient stroke evaluation or early discontinuation of monitoring. Episodes of PAF were detected in 16/64 (25%) patients with CS and 9/64 (14%) patients with SKC (p=0.12). Only 5 patients had episodes of PAF lasting > 30 seconds (3 with CS and 2 with SKC). Six of the 9 patients from the SKC group with PAF had small subcortical infarctions; the other 3 had cortical strokes ascribed to artery-to-artery embolism (2 with complex aortic plaques, 1 with severe vertebrobasilar stenosis). In patients under age 65, episodes of PAF were more common in the CS group (22% vs 3; p=0.07) while in patients age 65 or older the rates of detection were very similar (29% in CS vs 28% in SKC; p=0.9). Conclusions: Very short episodes of PAF are relatively common in patients with CS and with SKC, and their pathogenic significance is unclear. However, considering only patients under 65, a greater proportion of cryptogenic stroke patients had PAF, suggesting it might be pathogenic.

Background: Artificial intelligence (AI)-based analysis of the electrocardiogram (ECG) can estimate the probability of having atrial fibrillation (AF) while in sinus rhythm. We evaluated the association between the probability of AF using AI-ECG and incident ischemic stroke (I-Stroke) and cardiovascular (CV) mortality. Methods: We evaluated a community-based cohort of consecutive patients that sought primary care in Olmsted County, MN, from the years 1998 to 2000 followed-up through March 2016. Inclusion criteria were ages 40-79, an electrocardiogram within 2 years and complete CHA2DS2-VASc data. We excluded those with known atherosclerotic CV disease or AF. The AI network was developed in 180,000 patients (649,931 ECG). The primary endpoint was incident I-Stroke; secondary endpoint was I-Stroke or CV mortality; validated in duplicate. Multivariate cox models adjusted for age, sex, history of hypertension, diabetes, heart failure, and in CHA2DS2-VASc Score subgroups (categorized as low=0, moderate=1, and high risk ≥2). Results: We included 14,601 patients, mean age 53.2±13.08 (51% women, 95% whites), CHA2DS2-VASc score 1.3±1.2, median follow-up of 16.3 years, IQR 14.5-17.5, 727 (4.9%) had an I-Stroke and 1309 (8.9%) had I-Stroke or died from CV causes. When comparing the 5 th vs the 1 st quintile, those with high AI-ECG AF probability had increased risk for I-Stroke (HR 3.25, 95%CI 2.55-4.14, P-for trend <.0001 Figure-A ) and ischemic stroke or CV mortality (HR 5.73, 95%CI 4.69-6.99, P-for trend <.0001 Figure-B ). This remained significant after adjustments. Furthermore, AI-ECG AF probability was associated with I-Stroke across CHA2DS2-VASc risk levels (HR 2.43 for high, 2.33 for moderate, 1.98 for low CHA2DS2-VASc risk, all P<0.01). Conclusion: The AI-ECG AF algorithm predicts long-term I-Stroke and CV mortality and may refine CHA2DS2-VASc. These results highlight the utility of the AI enabled ECG when estimating I-Stroke or CV mortality risk.

Introduction: Congenital long QT syndrome (LQTS) is a sudden cardiac death-predisposing genetic disorder that has a characteristic electrocardiographic signature. Specific LQTS genotypes have been associated classically with different qualitative electrocardiographic ST - T wave patterns; however, clinical recognition of these patterns could be improved by an automated and quantitative method. We therefore aimed to use a novel, quantitative T wave morphological analysis technique from 12-lead surface ECGs to identify genotype specific LQTS ECG patterns. Methods: We analyzed a genotyped cohort of 420 patients (22 ± 15.7years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a custom T wave program to automatically detect subtle changes in T wave morphology across all 12 Leads. Classification was performed using the top three T wave features in a linear discriminant classifier by 10x10 cross-validation. Results: The top three discriminant features were (1) a steeper T wave left slope for LQT1 in lead II (2950 vs 1700 mV/ms, p < 0.0001), (2) a greater center of gravity in 1st segment of T wave in lead aVR in LQT1 (0.248 vs 0.209, p<0.0001) and (3) a smaller Tpeak - Tend/QT ratio in lead aVR in LQT1 (0.20 vs 0.24, p<0.0001). Using these top three features, we were able to successfully differentiate LQT1 and LQT2 in 79% of cases (figure 1). Conclusions: An automated morphological T wave analysis was able to discriminate between the two most common LQTS genotypes. This novel method enhances phenotypic characterization of LQT and could have a significant clinical impact on the diagnosis and management of patients with suspected LQTS.

Background: Transcatheter aortic valve replacement (TAVR) is often complicated by AV block requiring pacing, but whether patients recover AV conduction and whether the need for pacing affects outcomes is unknown. Methods: We retrospectively studied 534 patients who underwent TAVR at Mayo Clinic in Rochester, Minnesota between November 2008 and January 2015. Of these, 115 patients had implantable cardiac devices. Data was collected from subsequent device follow-ups to determine pacemaker dependency and percentage pacing. Results: 44 patients (8.2%) had devices implanted in the immediate post procedural period. The most common indications for implantation were high grade AV block (18 patients) and complete heart block (14 patients). 17/44 (38.6%) patients were pacemaker-dependent at implant and only 4 of these patients were no longer pacemaker-dependent during subsequent followup. The indication for pacing among these four patients was high grade atrio-ventricular block (2 patients), asystole (1 patient) and complete heart block (1). Among non-pacemaker-dependent patients, no significant change in the ventricular pacing percentages was noted over time. Survival was similar between patients who required device implantation post TAVR and those who did not after adjustment for age and gender (HR 0.73 [0.39-1.36], p=ns; figure) Conclusion: Patients requiring pacing after TAVR are often pacemaker-dependent at the time of implant. It is uncommon for patients who are dependent at the time of implant to regain AV conduction, but the need for cardiac pacing is not associated with increased mortality.

Background: The epicardial adipose tissue (EAT) has been identified as a sensitive marker of cardiometabolic risk. Recent evidence suggests efficacy of long-term statin therapy in reducing the EAT in patients with coronary artery disease. Whether the volume of EAT changes in patients with atrial fibrillation (AF) treated with a statin for a short time period is currently unknown. Methods: A population of AF patients undergoing pulmonary vein isolation was randomized to receive either 80 mg/day of atorvastatin (n=51, 44 males, age 55±10 years) or placebo (n=51, 41 males, age 56±10) for a 3 month period. EAT volume was assessed by cardiac computed tomography at baseline and after 3-month follow-up. Results: Patients randomized to statin treatment exhibited significant decreases in EAT volume (baseline vs follow-up: 105.8 ± 52.95 vs 103.05 ± 52.55 cm3, p = 0.05), C-reactive protein (2.97 ± 4.3 mg/L vs 2.03 ± 2.2 mg/L, p < 0.05), total cholesterol (182.5 ± 36.9 vs 129.3 ± 40.6 mg/dL, p < 0.001), and low density lipoprotein-cholesterol (111.88 ± 28 vs 61 ± 32.6 mg/dL, p < 0.001) at follow-up, while the body mass index did not vary (29.42 ± 5.2 vs 29.11 ± 4.8 kg/m2, p = 0.28). No changes were seen in the placebo group. Conclusions: Short-term high-dose statin therapy significantly reduced the volume of EAT in AF patients. EAT volume modulation might be an important mechanism by which statins may influence cardiovascular risk.

AbstractBackgroundThough infrequent, incomplete left atrial appendage closure (LAAC) may result from residual leaks. Percutaneous closure has been described though data is limited.MethodsWe compiled a registry from four centers of patients undergoing percutaneous closure of residual leaks following LAAC via surgical means or with the Watchman device. Leak severity was classified as none (no leak), mild (1–2 mm), moderate (3–4 mm), or severe (≥5 mm). Procedural and clinical success was defined as the elimination of leak or mild residual leak at the conclusion of the procedure or follow‐up, respectively.ResultsOf 72 (age 72.2 ± 9.2 years; 67% male) patients, 53 had undergone prior LAAC using the Watchman device and 19 patients surgical LAAC. Mean CHADS2‐VA2Sc score was 4.0 ± 1.8. The median leak size was 5 mm, range: 2–13). A total of 13 received Amplatzer Vascular Plug‐II, 18 received Amplatzer Duct Occluder‐II and 40 patients received coils. One underwent closure using a 21 mm‐Watchman. Procedural success was 94%. Zero surgical and nine Watchman patients (13%) had a residual leak at procedural‐end (five mild, three moderate, and one severe)—only one patient had no reduction in leak size. Overall leak size reduction was 94%. Two (3%) had intraoperative pericardial effusion. There were no device embolizations, device‐related thrombi, or procedural deaths. Clinical success was maintained at 94%. Two had cerebrovascular accidents—at 2 days (transient ischemic attack) and 10 months postprocedure. Two had major bleeding outside the 30‐day periprocedural window.ConclusionPercutaneous closure of residual leaks following left atrial appendage closure is feasible and associated with good outcomes. The procedural risk appears to be satisfactory.

AbstractAimsThe MicraTM transcatheter pacing system (TPS) (Medtronic) is the only leadless pacemaker that promotes atrioventricular (AV) synchrony via accelerometer‐based atrial sensing. Data regarding the real‐world experience with this novel system are scarce. We sought to characterize patients undergoing MicraTM‐AV implants, describe percentage AV synchrony achieved, and analyze the causes for suboptimal AV synchrony.MethodsIn this retrospective cohort study, electronic medical records from 56 consecutive patients undergoing MicraTM‐AV implants at the Mayo Clinic sites in Minnesota, Florida, and Arizona with a minimum follow‐up of 3 months were reviewed. Demographic data, comorbidities, echocardiographic data, and clinical outcomes were compared among patients with and without atrial synchronous ventricular pacing (AsVP) ≥ 70%.ResultsSixty‐five percent of patients achieved AsVP ≥ 70%. Patients with adequate AsVP had smaller body mass indices, a lower proportion of congestive heart failure, and prior cardiac surgery. Echocardiographic parameters and procedural characteristics were similar across the two groups. Active device troubleshooting was associated with higher AsVP. The likely reasons for low AsVP were small A4‐wave amplitude, high ventricular pacing burden, and inadequate device reprogramming. Importantly, in patients with low AsVP, subjective clinical worsening was not noted during follow‐up.ConclusionWith the increasing popularity of leadless pacemakers, it is paramount for device implanting teams to be familiar with common predictors of AV synchrony and troubleshooting with MicraTM‐AV devices.

INTRODUCTION: Cirrhosis is associated with cardiac dysfunction and distinct electrocardiogram (ECG) abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG and generate an AI-Cirrhosis-ECG (ACE) score that would correlate with disease severity. METHODS: A review of Mayo Clinic's electronic health records identified 5,212 patients with advanced cirrhosis ≥18 years who underwent liver transplantation at the 3 Mayo Clinic transplant centers between 1988 and 2019. The patients were matched by age and sex in a 1:4 ratio to controls without liver disease and then divided into training, validation, and test sets using a 70%-10%-20% split. The primary outcome was the performance of the model in distinguishing patients with cirrhosis from controls using their ECGs. In addition, the association between the ACE score and the severity of patients' liver disease was assessed. RESULTS: The model's area under the curve in the test set was 0.908 with 84.9% sensitivity and 83.2% specificity, and this performance remained consistent after additional matching for medical comorbidities. Significant elevations in the ACE scores were seen with increasing model for end-stage liver disease-sodium score. Longitudinal trends in the ACE scores before and after liver transplantation mirrored the progression and resolution of liver disease. DISCUSSION: The ACE score, a deep learning model, can accurately discriminate ECGs from patients with and without cirrhosis. This novel relationship between AI-enabled ECG analysis and cirrhosis holds promise as the basis for future low-cost tools and applications in the care of patients with liver disease.

Abstract Background Cardiovascular disease (CVD) has been identified as a major threat to maternal health in the US and UK with cardiomyopathy being one of the most common acquired CVD in the pregnant and postpartum period. Diagnosing cardiomyopathy in pregnancy is challenging due to an overlap of cardiovascular symptoms with normal pregnancy symptoms. Purpose The purpose of this study was to evaluate the effectiveness of an ECG based deep learning model in identifying cardiomyopathy among pregnant and postpartum women. Methods We utilized an ECG based deep learning model to detect cardiomyopathy in a cohort of pregnant or postpartum women seen at multiple hospital sites. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. Results 1,807 women were included. 7%, 10% and 13% had LVEF ≤35%, &lt;45% and &lt;50% respectively. The ECG based deep learning model identified cardiomyopathy with an AUC of 0.92 for left ventricular ejection fraction (LVEF) ≤35%, 0.89 for LVEF &lt;45% and 0.87 for LVEF &lt;50%. For LVEF ≤35%, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to white (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 and 0.72 respectively. Conclusions A deep learning model effectively identifies cardiomyopathy in pregnant or postpartum women, outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): This study was made possible using resources supported by the Mayo Clinic Women's Health Research Center and the Mayo Clinic Building Interdisciplinary Research Careers in Women's Health (BIRCWH) Program funded by the National Institutes of Health (NIH), grant number K12 HD065987. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.