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AbstractBackgroundRoutine defibrillation threshold testing (DFT) of transvenous implantable defibrillators (ICDs) has largely been in decline. In patients with non‐transvenous ICDs that utilize subcutaneous and pleural ICD leads, serial DFT testing can detect a significant number of failures. Data about the utility of follow‐up defibrillation safety margin testing (DSM) testing in pediatric patients and young adults with an epicardial ICD are lacking.MethodsPatients aged < 25 years old who underwent epicardial ICD placement at Mayo Clinic from 2014 to 2023 with at least one follow‐up DSM test were included. The patients were divided into a “routine” (R) and “clinically indicated” (CI) group based on the index of clinical concern. Inadequate DSM was defined as unsuccessful defibrillation at an output of less than 10 J below the maximum output of the device. The purpose of this study was to assess the utility of follow‐up DSM testing.ResultsAn epicardial ICD system was placed in 122 patients. A total of 26 patients met inclusion criteria and underwent a total of 47 DSM follow up tests. Inadequate DSM occurred in 1/33 (3%) in the R group and 2/14 (14%) DSM tests in the CI group. The median follow‐up period was 54 and 36 months for the R and CI group, respectively.ConclusionsOur data suggest that epicardial ICDs are reliable and routine follow‐up DSM testing may not be necessary for all patients. DSM testing should be performed in individuals with epicardial ICD systems when there is clinical concern about lead or coil performance.

Prolonged QT interval is an independent risk factor for all-cause mortality. However, evaluation of mortality associated to the implementation of a clinical decision support system to increase awareness and provide management recommendations has been challenging. Here we present our attempt to develop a model using only electronic data and different control groups.

Background Sex‐specific risk management may improve outcomes in congenital long QT syndrome (LQTS). We recently developed a prediction score for cardiac events (CEs) and life‐threatening events (LTEs) in postadolescent women with LQTS. In the present study, we aimed to develop personalized risk estimates for the burden of CEs and LTEs in male adolescents with potassium channel‐mediated LQTS. Methods and Results The prognostic model was derived from the LQTS Registry headquartered in Rochester, NY, comprising 611 LQT1 or LQT2 male adolescents from age 10 through 20 years, using the following variables: genotype/mutation location, QTc‐specific thresholds, history of syncope, and β‐blocker therapy. Anderson‐Gill modeling was performed for the end point of CE burden (total number of syncope, aborted cardiac arrest, and appropriate defibrillator shocks). The applicability of the CE prediction model was tested for the end point of the first LTE (excluding syncope and adding sudden cardiac death) using Cox modeling. A total of 270 CEs occurred during follow‐up. The genotype–phenotype risk prediction model identified low‐, intermediate‐, and high‐risk groups, comprising 74%, 14%, and 12% of the study population, respectively. Compared with the low‐risk group, high‐risk male subjects experienced a pronounced 5.2‐fold increased risk of recurrent CEs ( P <0.001), whereas intermediate‐risk patients had a 2.1‐fold ( P =0.004) increased risk . At age 20 years, the low‐, intermediate‐, and high‐risk adolescent male patients had on average 0.3, 0.6, and 1.4 CEs per person, respectively. Corresponding 10‐year adjusted probabilities for a first LTE were 2%, 6%, and 8%. Conclusions Personalized genotype–phenotype risk estimates can be used to guide sex‐specific management in male adolescents with potassium channel‐mediated LQTS.

Introduction: Over 100 FDA-approved medications, electrolyte perturbations, and many disease states can prolong the QT interval in up to 10% of patients. In contrast, approximately 1 in 2,000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation secondary to LQTS-causative mutations. Hypothesis: an artificial intelligence (AI) deep neural network (DNN) analysis of the 12-lead ECG can distinguish patients with LQTS from those with acquired QT prolongation. Methods: The study cohort included 1599 patients with genetically confirmed LQTS and over 2.5 million controls from Mayo Clinic’s ECG data vault. Every patient/control with ≥ 1 ECG above age- and sex- specific 99 th percentile values for QTc [> 460 ms for all patients (male/female) < 13 years of age, or > 470 ms for men and > 480 ms for women above this age] was included. An AI-DNN involving a multi-layer convolutional neural network was developed. To simulate screening conditions, patients were matched at a ratio of 1:2,000 (incidence of LQTS) or 1:200 (balance of LQTS vs acquired QT prolongation in a tertiary referral center). Results: Among the 1,599 patients with LQTS, 808 (> 50%) had ≥ 1 ECG with QTc above the aforementioned QTc thresholds (2,987 ECGs) compared to 361,069/2.5M controls (14% of Mayo Clinic patients getting an ECG, 989,313 ECGs). Following age- and sex- matching and splitting, the model successfully distinguished LQTS from those with acquired QT prolongation at 1:2,000 matching with an AUC of 0.937 (accuracy 89%, sensitivity 81%, specificity 90%, PPV 0.05, NPV 0.99). Furthermore, when matching at a rate that genetically-mediated QT prolongation would be encountered in clinic (1:200), the model still successfully separated the two groups (AUC 0.912, accuracy 88%, sensitivity 78%, specificity 89%, PPV 0.1, NPV 0.99). Conclusion: For patients with a QTc exceeding its 99 th percentile values seen in health, this novel AI-DNN 12-lead ECG distinguishes abnormal QT prolongation stemming from LQTS versus acquired QT prolongation with high performance characteristics (AUC > 0.93). Even when scaled to referral center and LQTS incidence ratios, a negative AI-DNN signal rules out the presence of a LQTS disease-causative mutation with 99% negative predictive value.

Introduction: Disease-causative variants in LMNA -encoded lamin A/C cause a penetrant dilated cardiomyopathy associated with conduction disease (CD) and ventricular arrhythmias (VA). Prior studies show conflicting results regarding the effect of sex, variant type, and CD on severity and sudden death risk in cardiac laminopathy. Methods: In this retrospective study, we utilized a single-center genetic arrhythmogenic cardiomyopathy registry (N=432) to identify patients with ultra-rare LMNA variants and ascertain the effect of sex, mutation type, and clinical presentation on disease severity. Advanced heart failure (AHF) events were defined as ventricular assist device placement or cardiac transplantation whereas VA events were defined as sudden cardiac arrest, sustained ventricular arrhythmia, or appropriate implantable cardioverter-defibrillator shock. Results: The LMNA variant-positive cohort (N=83) had an age of diagnosis of 36 ± 10 years with 80% showing a cardiac phenotype. No difference in the age at diagnosis (37 vs. 34 years; p=0.3) or prevalence of AHF events (14% vs. 18%; p=0.8) and VA events (34% vs. 36%; p=0.9) was observed between females (N=50) and males (N=33). Furthermore, there was no difference in age of diagnosis (37 vs. 33 years; p=0.1) or prevalence of AHF (14% vs. 12%; p=0.9) and VA events (33% vs. 34%; p=0.9) between individuals with missense (N=42) or frameshift (N=32) variants. Interestingly, in comparison to individuals that presented with isolated CD (N=11) those that first presented with left ventricular dysfunction or arrhythmias (atrial or ventricular) (N=41) were more likely to have VA events (9% vs. 53%; p=0.01) and AHF events (0% vs 29%; p=0.05). In addition, those with structural/arrhythmic presentation had earlier time to VA events (15 years vs. 22 years; p=0.04) and AHF events (23 vs undefined; p=0.09) than those with CD. Conclusions: Contrary to prior studies, in this large single-center cardiac laminopathy study, sex and mutation type were not associated with increased risk of AHF or VA events. However, patients presenting with isolated CD had a lower risk of AHF and VA events. Contemporary multi-center studies are needed to better ascertain risk factors associated with AHF and VA events in cardiac laminopathy.

Introduction: Recently, we demonstrated transcriptional down-regulation of hypertrophy pathways in myectomy tissue derived from patients with obstructive hypertrophic cardiomyopathy (HCM) despite translational activation of associated hypertrophy pathways. Previous studies of other forms of cardiac hypertrophy demonstrate hypertrophy pathways are activated via histone acetylation and histone deacetylase (HDAC) inhibitors prevented development of hypertrophy. Whether histone modifications regulate hypertrophy pathways in HCM remains unexplored. Methods: Chromatin immunoprecipitation sequencing (ChIP-seq) was performed on myectomy tissues from 40 patients with HCM and 23 controls to characterize differential histone marks for H3K4me3 (active promoter), H3K9ac (active promoter), H3K9me3 (heterochromatin), H3K27ac (active enhancer), H3K27me3 (repression) across the genome. The differential histone marks were integrated with RNA-sequencing (RNA-seq) data derived from the same patients. Results: Overall, we identified 2,912 (7%) differential H3K4me3 peaks impacting 5 pathways, 23,339 (21%) differential H3K9ac peaks impacting 87 pathways, 33 (0.05%) differential H3K9me3 peaks, and 58,837 (42%) differential H3K27ac peaks impacting 50 pathways. There was increased H3K27ac (q-value = 0.002) and H3K9ac (q-value = 0.009) at RAS-MAPK loci. Univariate analysis of concordance between H3K9ac with RNA-seq data showed activation of cardiac hypertrophy signaling (z-score=4.5; -log [BH p-value] = 3.1), while H3K27me showed down-regulation of cardiac hypertrophy signaling (z-score=-2.4; -log [BH p-value] = 2.4). Multivariate integration of the ChIP-seq data with RNA-seq data identified 3,189 transcripts concordant with all five histone marks impacting 254 pathways, 18 (7%) of which are involved in cardiac hypertrophy. Conclusions: Myectomy tissue from patients with obstructive HCM shows enrichment of histone modifications at cardiac hypertrophy loci with activation via H3K27 and H3K9 acetylation and repression via H3K27 methylation. Further studies are needed to understand the balance and temporal relationship between these competing regulators in HCM pathobiology and the potential for epigenetic therapies.

Introduction: Treatment options for patients with recurrent ventricular arrhythmias (VAs) refractory to pharmacotherapy and/or ablation are minimal. Although left cardiac sympathetic denervation (LCSD) is well established in long QT syndrome (LQTS) and catecholaminergic polymorphic ventricular tachycardia (CPVT), LCSD’s role in idiopathic ventricular fibrillation (IVF) is less clear. Here, we report our single-center experience regarding the early outcomes of LCSD in patients with IVF. Hypothesis: To determine the efficacy of LCSD on reducing VAs in IVF patients refractory to conventional therapy. Methods: A retrospective analysis of the electronic health record of patients with IVF between January 2000 to April 2023 was performed. Cardiac event rates [defined as arrhythmic syncope/seizure, appropriate implantable cardioverter defibrillator (ICD) therapy, sudden cardiac arrest (SCA), or sudden cardiac death (SCD)] before and after LCSD for each patient were calculated and a Wilcoxon signed rank test was performed. Results: In total, 94 patients (41% female) were classified as IVF. Overall, 15/94 patients (16%) had LCSD due to refractory VAs (60% male; mean age at LCSD 36 ± 20 years) despite beta-blocker treatment (100%) or combined drug therapy (67%). In total, 14 patients (15%) had an electrophysiology study performed before LCSD of which 10 (67%) had VAs ablation. All patients had a structurally normal heart and genetic testing performed in 14/15 patients (93%) confirmed absence of genetic cardiomyopathies and channelopathies. SCA was the sentinel event in 10 of 15 patients (67%); 14 patients (93%) had an ICD in place. The average follow-up time after LCSD was 3 ± 4 years. The VA event rate before LCSD was 4 ± 3 events per year and was reduced to 1 ± 3 per year (p<0.04) after LCSD. Patients had a median of 3.2 less events per year after LCSD (p=0.02) with 7 patients (47%) having no post-LCSD recurrences thus far while 3 (20%) patients were deemed LCSD non-responders with no reduction of event rate. Conclusions: LCSD represents a substrate-independent, anti-fibrillatory treatment option for patients with life-threatening, genetic arrhythmias. While not curative, the early outcomes of LCSD for patients with medically refractory IVF are promising.

Introduction: The ICD Sports Registry demonstrated that many athletes with ICDs can participate in competitive sports without death, failure to defibrillate, or physical injury. However, sudden cardiac arrest is more prevalent and has worse outcomes in Black compared to White individuals in the general population and among athletes. Outcomes of sports participation for Black athletes with ICDs have not been described. Aim: To describe the risk of arrhythmia and efficacy of ICDs in Black athletes. Methods: This is a post-hoc subgroup analysis of the ICD Sports Registry, which enrolled athletes with ICDs age 10 to 60, participating in organized, competitive sports, and followed them prospectively for four years. The endpoints and methods are reported previously. Results: Among the 440 athletes, 15 were Black and 410 white. Among the Black athletes, hypertrophic cardiomyopathy was the most common diagnosis (40%) and 87% were implanted for secondary prevention or syncope (vs 69% in White athletes). Most common sports were basketball (8), softball (4), and running (3), with 7 Black athletes competing on varsity/junior varsity/traveling teams. Among the Black athletes, there were no appropriate shocks during sports, among white athletes, 7% received an appropriate shock during sports. Among Black athletes, there was one appropriate shock event for ventricular tachycardia (7%), a VT storm in an athlete with idiopathic VF, non-sports-related. Among white athletes, 10% received appropriate shocks during non-sports activities. Two Black athletes (13%) received an inappropriate shock, one competition-related (sinus tachycardia) and the other for T wave oversensing at rest, vs 12% of white athletes. There were no sports-related deaths, arrests, or arrhythmia-related injuries in either group. Conclusions: Black athletes represented a small subset of the ICD Sports Registry. Reasons for this are unknown, but may include disparities in ICD implantation and/or in research recruitment/ participation. These Black athletes experienced both appropriate and inappropriate shocks as did white athletes. These data do not suggest a signal for harm for competitive sports participation for Black athletes with ICDs but larger studies are needed.

Background: The incidence and causes of sudden cardiac death (SCD) among young competitive athletes impact prevention strategies yet remain incompletely understood. Methods: National Collegiate Athletic Association (NCAA) athlete deaths from 7/1/2002-6/30/2022 were identified through 4 independent databases/search strategies. Autopsy reports and medical history were reviewed by an expert panel to adjudicate causes of SCD. Poisson regression was used to calculate incidence rate ratios (IRR) for 5-year intervals over the 20-year study. Results: A total of 143 SCD cases were identified among 1102 athlete deaths and 9,106,516 athlete-years (AYs). The incidence of SCD among NCAA athletes was 1:63,682 AYs [95% CI 1:54,065, 1:75,010]. Incidence was higher in males compared to females (1:43,348 vs. 1:164,504 AYs) and Black compared to White athletes (1:27,217 vs. 1:74,581 AYs). The highest incidence of SCD was among Division 1 male basketball players (1:8,188; White 1:5,848; Black 1:7,696 AYs). The incidence rate for SCD decreased over the study period (5-year IRR 0.71 [95% CI 0.61,0.82]), whereas the rate of non-cardiovascular deaths remained stable (5-year IRR 0.98 [95% CI 0.94,1.04]; Figure 1A ). Autopsy-negative sudden unexplained death (AN-SUD, 19.5%) was the most common post-mortem exam finding, followed by idiopathic left ventricular hypertrophy/possible cardiomyopathy (CM, 16.9%) and hypertrophic CM (12.7%) in cases with enough information for adjudication (118/143, Figure 1B ). There were 8 cases of myocarditis, with none attributed to COVID-19 infection. SCD events occurred most commonly during exertion among athletes with coronary artery anomalies (100%) and arrhythmogenic CM (83%), whereas AN-SUD and hypertrophic CM had a higher proportion of non-exertional SCD (44% and 40%, respectively). Conclusions: The incidence of SCD in college athletes has decreased. Male sex, Black race, and basketball are associated with a higher incidence of SCD.

Abstract Aims ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.