Citations

Abstract Intro Energy harvesters that convert biomechanical energy into electrical power could be used for implantable devices, providing a near infinite energy solution. While various energy harvesters with piezoelectric or triboelectric elements have been designed and applied to the cardiac environment, none have been successfully integrated into leadless pacemakers due to their size constraints or insufficient energy generation. Purpose This study aimed to develop a proprietary inertial piezoelectric energy harvester (PEH) capable of generating the energy required for a self-sustaining leadless pacemaker from the beating of the heart. Method Human heart dynamics were tested using ultra-fast MRI scans of 20 patients, which were analyzed for displacement of the right ventricular (RV) apex, mid- to low septum, and the right atrial (RA) walls. The data was used to design the PEH, which was evaluated in thirteen (13) male swine. In addition, epicardial MEMS-based sensors recorded the acceleration, angular positions, and gravity in 3 directions. The sensors were placed at the apex of the RV, LV Apex, LV free wall, and the RA using adhesive means. The data collected from the animals was used to calculate the power produced by the PEH in the different regions of interests and to create a bench model to optimize the PEH design further. The PEH was then tested in-vivo in an ovine model. Results The heart contractility creates harmonic acceleration in the heart tissue, transmitting this vibration into the PEH, resulting in the harvester acceleration of 0.75 g (±0.25g). When the PEH is subject to vibrations, the PEH oscillates using kinetic forces exerted on the piezoelectric beam through a proof mass, transforming these mechanical forces into electric energy. The electrical energy produced using this PEH is 6µW (± 2µW), which is sufficient to power a leadless pacemaker, providing pacing therapy of 100% pacing, at 60 BPM, with heart impedance of 600 (±200), and a pacing voltage reaching 2V, with sufficient excess energy to maintain charge of a secondary battery used for communication and advanced pacemaker functions. This was then tested in a pre-clinical setup, using an ovine model (results to be presented in the future). Conclusion This work demonstrates that a proprietary inertial PEH can use the contraction energy of the heart to generate electrical energy. The design of the PEH was based on the analysis of human heart kinetics using MRI scans and in-vivo recordings from pigs’ hearts. This design of an effective energy harvester can produce enough energy to power a leadless pacemaker. The design can fit in a small capsule that can be used in a leadless pacemaker that will be implanted in the RV providing sensing and pacing as well as daily communication enabling advanced pacemaker functions. The PEH is currently being tested in an ovine model, demonstrating its safety and performance.

Abstract Background The extravascular implantable cardioverter defibrillator (EV-ICD) was designed to avoid lead-related complications associated with transvenous systems and deliver features such as anti-tachycardia pacing (ATP) and reduced generator size that provide an advantage over a subcutaneous ICD (S-ICD). As such, the EV-ICD is an attractive alternative for younger patients with genetic heart diseases (GHDs) who are not in need of permanent pacing. Objective To examine the indications, complications, and the clinical outcomes of patients with GHDs who received an EV-ICD. Methods A retrospective review of the electronic record was done for all GHD patients evaluated in genetic heart rhythm and/or cardiomyopathy clinics who had an EV-ICD implanted between 1/2021 and 2/2025. Basic demographic, ICD indications, complications, and therapy data were then extracted for all study patients. Results Overall, 23 patients (35% female; mean age 39 ± 16 years; 61% with a disease-causative genetic variant; mean LVEF 57%) with an underlying GHD (hypertrophic cardiomyopathy in 9, arrhythmogenic cardiomyopathy in 8, idiopathic ventricular fibrillation in 3, dilated cardiomyopathy in 2, and long QT syndrome in 1) had an EV-ICD implanted. For the majority, an EV-ICD was implanted for primary prevention (15/23; 65%) and represented their first ICD system (21/23; 91%). The success rate of the procedure was 96% with only one abandoned procedure because of poor R-wave sensing. Inadvertent intraprocedural lead placement in the pericardial space occurred in 1 patient with previous thoracotomy and surgically placed epicardial lead without sequelae or need for revision. Over a mean follow-up of 8±9 months, the rate of complications was low (1/23; 4%) with one electrode displacement and this patient underwent defibrillation threshold testing (DFT), the device properly detected VF and emitted a successful shock 30 J. Thus far, 2 patients have received appropriate VT/VF-terminating therapies (3 ICD shocks, 0 ATP) with successful conversion to sinus rhythm. One patient died following an electrical storm. One patient had 3 inappropriate ICD shock for sinus tachycardia while exercising followed by 1 successful shock for VF. Conclusion In this early experience, EV-ICDs appear to be safe and effective in younger patients with genetic arrhythmogenic and hypertrophic cardiomyopathies. Future studies are needed to better understand the utility of EV-ICDs in patients with cardiac channelopathies and advantages of EV-ICDs over transvenous and S-ICD systems in patients with an array of sudden death-predisposing GHDs.

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.

BACKGROUND: MODULAR antitachycardia pacing (ATP), a multicenter, international trial, assesses a modular cardiac rhythm management system: a subcutaneous implantable cardioverter defibrillator in wireless communication with a leadless pacemaker (LP) capable of pace-terminating ventricular tachycardia. METHODS: Enrolees had one or more clinical risk factors for ventricular tachycardia and did not require chronic pacing. Complications included prespecified major LP system- and procedure-related complications, and any complication related to the LP, subcutaneous implantable cardioverter defibrillator, implantation, or study protocol. Survival analysis was performed to identify complication-free rates, therapy delivery, and all-cause mortality. RESULTS: The 297 patients enrolled had an ejection fraction of 35±13%, 43% secondary prevention indications, and 59% with prior ventricular arrhythmias. Of 286 patients undergoing LP implantation (100% success), 251 patients completed 12-month follow-up. Mortality rate was 6%, with none related to the implant procedure. Median follow-up duration was 23.4 months (interquartile range, 17.9–28.1). The LP major complication-free rate was 97.2%, exceeding the performance goal. The overall LP+ subcutaneous implantable cardioverter defibrillator system-related complication-free rate was 88.5%. Appropriate tachyarrhythmia-therapy (ATP+shock) rates were 14.4%, and appropriate shock rates were 8.5%. Inappropriate total tachyarrhythmia therapy was 9.5% of which 8.5% were shocks. ATP was 67.3% successful in terminating ventricular arrhythmia episodes and accelerated ventricular arrhythmias in 10.1% of episodes. Overall therapy burden (ATP+shock) was 96/100 patient-years, of which 44/100 patient-years was for shock delivery. CONCLUSIONS: One-year outcomes of the first modular pacing-defibrillator system reveal low system and LP complication rates and good ATP efficacy rates, suggesting that the modular cardiac rhythm management is a viable alternative to single-chamber implantable cardioverter defibrillators using low-energy pacing capability without the need for transvenous leads. CLINICAL TRIAL REGISTRATION: URL: https://clinicaltrials.gov/ ; Unique identifier: NCT04798768.

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP>15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Background: Atrial arrhythmia recurrence after surgical ablation remains challenging to predict and integrating novel biomarkers may improve risk stratification. Objective: To evaluate whether combining preoperative transthoracic echocardiography (TTE) with artificial intelligence-enabled ECG (AI-ECG) scores enhances prediction of atrial fibrillation/flutter (AF/AFl) recurrence post surgical ablation. Methods: We retrospectively analyzed 1,696 patients undergoing Surgical AF/AFl Ablation between 2006 and 2025 with preoperative TTE and post-blanking ECGs. Clinical variables, TTE indices, and AI-ECG scores (AF probability, ECG-estimated age, HFpEF, LV dysfunction, and aortic stenosis scores) were assessed. The primary outcome was time to AF/AFl recurrence. Univariate/multivariable Cox models and a Random Survival Forest (RSF) model (80:20, training: testing) were developed to identify predictors. Results: Of 1,696 patients (mean age 67.3±10.2 years; 61.7% male) undergoing surgical AF/AFl ablation, 949 (56%) experienced AF/AFl recurrence during a median follow-up of 3.14 years. Patients with recurrence had larger left atria (mean LA area 30.4 vs 24.5 cm 2 , p < 0.001), more diastolic dysfunction (mitral inflow E-wave velocity 1.015 m/s vs 0.896 m/s , p < 0.001), and adverse AI-ECG biomarkers. In multivariable analysis, independent predictors of recurrence included a higher ECG-AF score (p < 1 x 10 -300 ), an older AI-ECG age (p = 0.0002), LA area (p = 0.046), body mass index (p = 0.036), and elevated diastolic blood pressure (HR 1.008 per mmHg, 95% CI 1.002 – 1.014; p = 0.010). The final Cox model achieved a C-index of ~0.67 and stratified patients into risk quartiles with 3-year freedom-from-arrhythmia rates of ~85% (lowest-risk) vs ~43% (highest-risk). An RSF model yielded a slightly higher test C-index (≈0.69), suggesting modest improvement with non-linear modeling. Conclusions: Preoperative AI-ECG biomarkers (AF probability, age discordance) and TTE markers of atrial remodeling independently predicted AF/AFl recurrence after surgical AF/AFl ablation and integration of these metrics improved risk stratification.

Background: Disease-causative variants in LMNA -encoded lamin A/C cause the laminopathies, a heterogeneous group of diseases variably resulting in arrhythmogenic/dilated cardiomyopathy (ACM/DCM), lipodystrophy, muscular dystrophy, and progeria. The artificial intelligence (AI)-derived electrocardiographic age gap (AI-EAG), determined by the discrepancy between a patient’s artificial AI-enabled electrocardiogram (ECG) predicted biological age versus their chronological age, is accelerated in laminopathy. Thus, we sought to determine if the AI-EAG serves as a prognostic marker in patients with cardiac laminopathy. Methods: Retrospective analysis of 1,049 genotype-positive patients with genetic ACM/DCM was used to identify those with pathogenic/likely pathogenic (P/LP) variants in LMNA . After the exclusion of those who lacked a 12-lead ECG while in sinus rhythm, a previously trained AI-ECG age algorithm was used to determine the AI-EAG by subtracting the patient’s chronological age from the AI-ECG derived biological age. The AI-EAG was then correlated with a combined outcome of major ventricular arrhythmia [sudden cardiac arrest, sustained ventricular tachycardia, and appropriate implantable cardioverter-defibrillator shocks], heart transplantation, and cardiovascular death. Results: Overall, 147/1,049 (14%) patients with genetically-mediated ACM/DCM had a P/LP variant in LMNA . Of these, 80/147 (54%) LMNA variant-positive patients (52% female, mean chronological age 36 ± 15 years) had ECGs suitable for AI-EAG analysis. Most (52/80; 65%) had an AI-EAG >10 years with a mean AI-EAG of 17 ± 13 years. Of note, the AI-EAG was greater in those with a clinical cardiac phenotype (20 ± 14 vs. 13 ± 10 years; p = 0.011). As a continuous variable, an increased AI-EAG was associated with increased risk of the composite outcome at a median follow-up of 20 months (HR 1.036; 95% CI 1.004–1.068; p = 0.026) and AI-EAG > 10 and > 20 years were both associated with elevated risk (HR 4.272; 95% CI 1.388–13.147; p = 0.011 and HR 3.732; 95% CI 1.505–9.255; p = 0.004, respectively). Conclusion: In cardiac laminopathy, an increased AI-EAG was common and correlated with adverse cardiovascular outcomes. Non-invasive ascertainment of electrocardiographic aging by AI may provide a novel prognostic marker in cardiac laminopathy. Future studies are needed to validate this finding and further define the role of the AI-EAG in the risk-stratification of patients with cardiac laminopathy.

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (>14), left atrial volume index (>34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (>2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.

Background: Tricuspid regurgitation (TR) is associated with significant morbidity, but individualized treatment and timing of intervention are yet to be defined. We previously proposed and validated artificial intelligence electrocardiography (AI-ECG) scores for various diseases. Whether incorporating AI-ECG information adds to risk stratification above and beyond classical clinical parameters is unknown. Objectives: To identify AI-ECG scores independently associated with all-cause mortality in chronic TR and to create a composite AI-ECG risk score. Methods: Patients with a first echocardiographic diagnosis of ≥moderate TR between 2005-2016 and an ECG within ±15 days were included. The Tricuspid Regurgitation Impact on Outcome (TRIO) score was calculated as previously described based on 8 simple parameters (age, sex, severe TR, heart failure, lung disease, heart rate, creatinine and AST). AI-ECG probabilities of low ejection fraction, aortic stenosis, amyloid, cirrhosis, atrial fibrillation and hypertrophic cardiomyopathy were estimated by existing algorithms; the first 4 were associated with all-cause mortality and a composite AI-ECG risk score was calculated (0-4; 1 point for each AI-ECG above specific threshold). Results: A total of 12,377 pts were included (age 72±13 yrs, 55% women). Over a median follow-up of 1.3 yrs (IQR 0.1 to 4.4), 7,121 pts (58%) died. Mortality risk increased significantly with higher composite AI-ECG risk score (Fig. 1A). On multivariable analysis (proportional hazards method), the composite AI-ECG risk score remained independently associated with death, even after adjusting for age, sex, high NT-proBNP levels (>2,500 pg/mL), right ventricular systolic pressure (RVSP), diuretic use and TRIO risk score category (Fig. 1B). Conclusions: Patients with ≥moderate TR show progressively worse survival with increasing composite AI-ECG risk score. Incorporating AI-derived information from a simple and inexpensive ECG may identify patients with the highest risk of mortality beyond clinical risk scores as TRIO or classical clinical, laboratory and echocardiographic parameters. Prospective validation of this approach needs to be tested in future trials.

Background: Arrhythmogenic and dilated cardiomyopathies (ACM/DCM) are associated with adverse cardiovascular outcomes, including sudden death. Artificial intelligence (AI)-enabled electrocardiographic aging algorithms estimate biological age from 12-lead tracings and predict cardiovascular outcomes in the general population. However, their utility in genetic ACM/DCM has not been studied. Methods: Retrospective review of 1,045 genotype-positive ACM/DCM patients was used to identify those with a pathogenic/likely pathogenic variant in ClinGen-classified definitive/strong evidence ACM/DCM-susceptibility genes. Following exclusion of patients without a baseline ECG for analysis (e.g. paced rhythms), the AI-ECG age gap (AI-EAG) was calculated by subtracting chronological from AI-ECG age. Genes were grouped into functional categories: desmosomal ( PKP2, DSP, DSG2, DSC2, JUP ), nuclear envelope ( LMNA ), sarcomeric ( TTN, MYH7, TNNT2 ), cytoskeleton/Z-disc ( FLNC, DES, DMD, BAG3 ), and regulatory/ion channel ( PLN, RBM20, SCN5A ). Cardiac MRI data included left ventricular structure, function, and late gadolinium enhancement (LGE) presence and pattern. Mann-Whitney U was used for group comparisons; results are shown as median (IQR). Results: Overall, 824 patients (52.8% male; median age of 40.7 years, IQR 26.5–53.4; and median AI-EAG of 7.49 years, IQR 0.43–15.26) were included. DES variant-positive patients had the highest (19.98 years, IQR 9.66–23.21) and BAG3 variant-positive patients the lowest (–0.95 years, IQR -6.05–7.61) AI-EAGs. Among gene groups, nuclear envelope had the highest (15.12 years, IQR 6.79–25.17) and sarcomeric the lowest (5.75 years, IQR -0.69-14.01) AI-EAGs. Cardiac MRI was available for 538/824 (65%) of which 233/538 (43.3%) had LGE. No significant difference in AI-EAG was observed between LGE-positive and -negative patients (6.99 vs. 7.94 years; p=0.831). However, several LGE patterns were associated with significantly higher AI-EAGs: subepicardial (9.88 vs. 6.84; p=0.004), transmural (13.83 vs. 7.25; p=0.012), apical (12.89 vs. 7.17; p=0.002), and septal (8.91 vs. 7.19; p=0.016). Conclusion: AI-EAG varies by gene and functional group, possibly reflecting gene-specific myocardial remodeling. While certain LGE patterns were linked to increased biological aging, overall LGE status was not. These findings suggest AI-EAG may capture disease activity that is distinct from LGE and could serve as a complementary, non-invasive biomarker in genetic ACM/DCM.