Friedman, Paul A.

Introduction: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic disorder characterized by fibrofatty replacement of myocardial tissue, creating areas of electrical heterogeneity and increasing the risk of ventricular arrhythmias (VAs) and sudden cardiac death (SCD). Catheter ablation is commonly performed in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) for management of ventricular arrhythmias (VAs). Whether catheter ablation affects cardiac function post-procedurally is uncertain. Research Question: Does catheter ablation for ARVC result in acute change in cardiac function? Goals/Aims: To assess the impact of catheter ablation for VAs on cardiac function in the acute post-procedure setting. Methods: This retrospective study included patients with ARVC who underwent catheter ablation for VAs at Mayo Clinic. Cardiac function was assessed on pre- and post-procedure transthoracic echocardiograms (TTEs). Post-procedure TTEs were obtained within 4 weeks after the ablation procedure. Right ventricular (RV) enlargement and systolic dysfunction were evaluated as continuous variables (0=none, 1=mild, 2=mild-moderate, 3=moderate, 4=moderate-severe, 5=severe). Results: The study included 32 patients who underwent a total of 44 ablations. Mean age at the time of ablation was 49±16 years, and 24 (75%) were male. After a mean follow-up duration of 2.6±5.5 days, there was a significant worsening in mean RV dysfunction (1.6±1.5 to 2.0±1.4, P = 0.001). There were no significant changes in mean left ventricular ejection fraction (LVEF), left ventricular (LV) dimensions, and RV size. 6 (14%) patients had a >5% decline in LVEF, and 14 (32%) patients had worsening RV function. Transient inotropic support was required in 4 (9%) patients. Conclusion(s): This study is the first to assess the acute impact of catheter ablation for VAs on cardiac function in ARVC patients. These results underscore the importance of careful post-procedure monitoring and planning of the ablation target in these patients to avoid excessive ablation and therefore impairment in cardiac function.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

Abstract Background Heart failure (HF) and atrial fibrillation (AF) are intertwined, sharing common risk factors and influencing each other in a reciprocal manner. While left ventricular diastolic dysfunction (LVDD) stands as a hallmark of HF's hemodynamic profile, its direct correlation with heightened AF risk remains uncertain. Our recent validation of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm for LVDD detection has opened new avenues. Yet, whether AI-ECG-detected LVDD correlates with long-term AF risk remains unexplored. Methods We performed a retrospective study among all patients with a comprehensive LVDD assessment from the test population of the AI-ECG LVDD study between September 2001 and June 2022. Patients with prior documented AF according to health records or previous ECG were excluded. Patients were classified by AI-ECG as normal diastolic function (DF), grade 1 (G1), grade 2 (G2), or grade 3 (G3) LVDD. We assessed the risk of AF across AI-ECG LVDD grade, with normal DF as reference, with competing risk models. Results Of 97,023 patients free of AF at baseline, 63,533 (65.4%), 10,776 (11.1%), 18,940 (19.5%), and 3,774 (3.9%) were classified as normal DF, G1-LVDD, G2-LVDD, and G3-LVDD, respectively. Worse AI-ECG LVDD was associated with older age, lower left ventricular ejection fraction (LVEF), and higher rates of HF, diabetes, hypertension, chronic pulmonary disease, renal failure, and cardiovascular disease (p-of-trend&lt;0.001 for all). Over a median follow-up of 4.8 years (interquartile range 1.8-9.2 years), new-onset AF occurred in 6,795 (7.0%) patients. In multivariable survival analysis, adjusted to multiple risk factors including LVEF and clinical HF, G2 and G3 LVDD, representing patients with high filling pressures, were independently associated with a higher AF-risk [G2-LVDD: adjusted hazard ratio (aHR) 1.64 (95% confidence interval (95%CI) 1.52-1.73), G3-LVDD: aHR 2.24 (95%CI 2.02-2.48)]. Conclusion The AI-ECG LVDD algorithm independently predicts long-term AF risk, regardless of LVEF and clinical HF status, highlighting its potential for identifying high-risk populations and guiding proactive monitoring and management.

Abstract Background The extravascular implantable cardioverter defibrillator (EV ICD) has recently emerged as an option for guideline indicated ICD patients. Results from the EV ICD Pilot and Pivotal studies demonstrated that the device can provide safe and effective therapy through chronic follow up. Lead placement outside the vasculature has the potential to reduce systemic infections. Infection occurrence and management details from the Pivotal are reported. Purpose To report on details of infection occurrence and management from the EV ICD Pivotal study. Methods The EV ICD Pivotal study was an international, prospective, single-arm, premarket clinical study. Patients with a class I or IIa indication for a single-chamber ICD per ESC or ACC/AHA/HRS guidelines were enrolled. Results In the EV ICD Pivotal study, 316 patients had an implant attempt, (74.7% male, age 53.8±13.1 years, 82% primary prevention, LVEF of 38.9%±15.4%, and NYHA Class I [23.7%] or II/III [65.5%]), of which 15 had a system or procedure-related infection (4.7%, 9 - 737 days post-implant) through an average 16.2 months follow-up. Of these infections, 8 were classified as major complications, 3 as minor, and 4 as observations. Most (n=9) infections were addressed through medication with or without wound care (2.8% of patients) while 6 (1.9% of patients) resulted in system removal. There was a single lead-related infection addressed through system explant. However, there were no reports of mediastinitis, sepsis, or endocarditis. No system removal resulted in sequelae. Conclusions Infection rates observed in the EV ICD Pivotal study were similar to those reported in the subcutaneous implantable cardioverter defibrillator. The results from this study support that EV ICD related infections are treatable with antibiotic therapy or system removal. No system or procedure-related systemic infections such as mediastinitis, sepsis, or endocarditis have been reported in the Pivotal study.

Abstract Background Cardiovascular disease remains the leading cause of death and burden globally, warranting the need to explore novel predictors of major adverse cardiovascular events (MACE) using new technologies. Artificial Intelligence (AI)-enhanced ECG (AI-ECG) algorithms can provide information on cardiovascular health of individuals, independent of conventional risk factors. Purpose The current study aimed to understand whether a previously described AI-ECG sex estimation algorithm could be applied for risk stratification of major adverse cardiovascular events (MACE), with a focus on a population without previous history of MACE. Methods Patients having 12-lead ECGs between 2018-2019 were included and followed up until August 2023. Exclusion criterion was a prior history of MACE. AI-ECG sex estimation was determined using a previously described algorithm predicting sex based on 12-lead ECGs. The algorithm generates outputs ranging from 0 to 1, representing the estimated probability of being male. Outputs above 0.5 were considered as AI-ECG-estimated male, and below as AI-ECG-estimated female. This was compared with biological sex to determine whether the two matched. Inconsistency between AI-ECG estimated sex and biological sex was defined as discordance. MACE was defined as myocardial infarction, coronary revascularization, non-fatal stroke, and all-cause death. Results Among 80,578 subjects (mean age 59.1 ± 16.1 years, 52.4% female), 9.7% exhibited sex discordance in AI-ECG estimation. Discordance was associated with a 56% higher risk of MACE (HR, 1.56, 95% CI 1.46-1.65, p &lt;0.001), and remained significant in multivariable analysis with traditional risk factors (HR 1.35, 95% CI 1.27-1.43, p &lt;0.001) (Figure 1). Sex-specific analysis showed increased MACE risk in both discordant males and females (Figure 2). Conclusion In patients with no history of MACE, discordance in AI-ECG sex estimation is associated with increased risk of MACE. These findings suggest the potential of AI-ECG sex estimation algorithm for risk assessment, especially in patients with no history of MACE.Figure 1.Cox proportional hazard modelFigure 2.Kaplan-Meier of MACE by sex

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (&gt;moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with &gt;moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P &lt; 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P &lt; 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

BACKGROUND: Substernal lead placement of the extravascular implantable cardioverter defibrillator (EV ICD) permits both defibrillation at thresholds similar to those seen with transvenous implantable cardioverter defibrillators and effective anti-tachycardia pacing (ATP) while avoiding the vasculature and associated complications. The global Pivotal study has shown the EV ICD system to be safe and effective through 6 months, but long-term experience has yet to be published. Our aim was to report the performance and safety of the EV ICD system throughout the study. METHODS: The EV ICD Pivotal study was a prospective, global, single-arm, premarket clinical study. Individuals with a Class I or IIa indication for a single-chamber implantable cardioverter defibrillator per guidelines were enrolled. Freedom from major system- or procedure-related complications and appropriate and inappropriate therapy rates were assessed through 3 years with the Kaplan-Meier method. ATP success was calculated from simple proportions. RESULTS: An implantation was attempted in 316 patients (25.3% female; 53.8±13.1 years of age; 81.6% primary prevention; left ventricular ejection fraction, 38.9±15.4%). Of 299 patients with a successful implantation, 24 experienced 82 spontaneous arrhythmic episodes that were appropriately treated with ATP only (38, 46.3%), shock only (34, 41.5%), or both (10, 12.2%) for a Kaplan-Meier–estimated rate of first any appropriate therapy of 9.2% at 3 years. ATP was successful in 77.1% (37/48) of episodes, and ATP use significantly increased from discharge to last follow-up visit ( P <0.0001). Shock therapy was successful in 100% (27/27) of discrete, spontaneous ventricular arrhythmias. The inappropriate shock rates at 1 and 3 years were 9.8% and 17.5%, respectively, with P-wave oversensing the predominant cause. No major intraprocedural complications were reported, and the estimated freedom from system- or procedure-related major complications was 91.9% at 1 year and 89.0% at 3 years. The most common major complications were lead dislodgement (10 events; n=9 patients, 2.8%), postoperative wound or device pocket infection (n=8, 2.5%), and device inappropriate shock delivery (n=4, 1.3%). Twenty-four system revisions were performed as a result of major complications related to the EV ICD system or procedure. CONCLUSIONS: From implantation to study completion, the EV ICD Pivotal study demonstrated that a single integrated system with an extravascular lead placed in the substernal space maintains high ATP success, effective defibrillation, and a consistent safety profile. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04060680.

Abstract Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild-type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan–Meier was used to estimate survival. Results Median age of the cohort was 67 [inter-quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two-fold risk of all-cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. Conclusions Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.

Abstract Aims The extravascular implantable cardioverter-defibrillator (EV ICD) has been shown to be safe and effective for patients at risk of sudden cardiac death, but little is known about EV ICD lead removal in humans. This analysis aimed to characterize the EV ICD lead removal experience thus far. Methods and results This was a retrospective analysis of lead removals from the EV ICD Pilot, Pivotal, and Continued Access Studies. Patients with a successful EV ICD implant who underwent lead removal were included. The main objective was lead removal success. Ancillary objectives included characterizing technique used, procedure complications, and reimplantation status. An EV ICD system was successfully implanted in 347 patients across the 3 studies (25.9% female; 53.4 ± 13.3 years; left ventricular ejection fraction: 39.7 ± 15.9). Of these patients, 29 (8.4%) underwent lead removal with a mean lead dwell time of 12.6 ± 14.3 months (0.2–58.4). The main reason for lead removal was lead dislodgement (n = 9, 31.0%). Lead removal was successful in 27/29 (93.1%) cases [100% (19/19) success rate &lt;1 year and 80% (8/10) success rate &gt;1 year post-implant]. Simple traction was used in 22/26 (84.6%) and extraction tools in 4/26 (15.4%) successful cases where technique was known. No complications were reported for any of the removal procedures. All 11 EV ICD reimplant attempts were successful. Conclusion Complete removal of the EV ICD lead was successful in 93.1% of cases, and simple traction was sufficient in most instances. Based on these results, lead removal from the substernal space was safe and achievable up to 3 years post-implant.