Friedman, Paul A.

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.

Background: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered hemodynamics, and risk for incident AF could be identified from a single 12-lead ECG using a novel artificial intelligence (AI)-enabled ECG analysis. Methods: Patients with heart failure and preserved ejection fraction (n=613) underwent AI-enabled ECG analysis, echocardiography, and cardiac catheterization. Individuals were grouped by AI-enabled ECG probability of contemporaneous AF, taken as an indicator of underlying LA myopathy. Results: Structural heart disease was more severe in patients with higher AI-probability of AF, with more left ventricular hypertrophy, larger LA volumes, and lower LA reservoir and booster strain. Cardiac filling pressures and pulmonary artery pressures were higher in patients with higher AI-probability, while cardiac output reserve was more impaired during exercise. Among patients with sinus rhythm and no prior AF, each 10% increase in AI-probability was associated with a 31% greater risk of developing new-onset AF (hazard ratio, 1.31 [95% CI, 1.20–1.42]; P <0.001). In the population as a whole, each 10% increase in AI-probability was associated with a 12% greater risk of death (hazard ratio, 1.12 [95% CI, 1.08–1.17]; P <0.001) during long-term follow-up, which was no longer significant after adjustments for baseline characteristics. Conclusions: A novel AI-enabled score derived from a single 12-lead ECG identifies the presence of underlying LA myopathy in patients with heart failure and preserved ejection fraction as evidenced by structural, functional, and hemodynamic abnormalities, as well as long-term risk for incident AF. Further research is required to determine the role of the AI-enabled ECG in the evaluation and care of patients with heart failure and preserved ejection fraction.

AbstractBackgroundData regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation following mitral valve surgery (MVS) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of mitral annuloplasty or prosthetic valves.ObjectiveTo investigate the characteristics, safety, and outcomes of radiofrequency CA in patients with prior MVS and ventricular arrhythmias (VA).MethodsWe identified consecutive patients with prior MVS who underwent CA for VT or PVC between January 2013 and December 2018. We investigated the mechanism of arrhythmia, ablation approach, peri‐operative complications, and outcomes.ResultsIn our cohort, 31 patients (77% men, mean age 62.3 ± 10.8 years, left ventricular ejection fraction 39.2 ± 13.9%) with prior MVS underwent CA (16 VT; 15 PVC). Access to the left ventricle was via transseptal approach in 17 patients, and a retrograde aortic approach was used in 13 patients. A combined transseptal and retrograde aortic approach was used in one patient, and a percutaneous epicardial approach was combined with trans‐septal approach in one patient. Heterogenous scar regions were present in 94% of VT patients and scar‐related reentry was the dominant mechanism of VT. Forty‐seven percent of PVC patients had abnormal substrate at the site targeted for ablation. Clinical VA substrates involved the peri‐mitral area in six patients with VT and five patients with PVC ablation. No procedure‐related complications were reported. The overall recurrence‐free rate at 1‐year was 72.2%; 67% in the VT group and 78% in the PVC group. No arrhythmia‐related death was documented on long‐term follow‐up.ConclusionCA of VAs can be performed safely and effectively in patients with MVS.