BackgroundAtrial septal defects (ASD) are associated with an increased incidence of atrial arrhythmias, but their electrophysiological consequences are poorly defined. We hypothesised that conduction and repolarisation would be preferentially altered in the right atrium of ASD patients.ObjectiveTo quantify atrial conduction and repolarisation in ASD patients and determine the impact of structural remodelling on restitution properties.MethodsPatients with an ASD (n = 22) underwent bi-atrial electroanatomic mapping and quantification of effective refractory periods, longitudinal and transverse local conduction. The control group comprised 24 patients without an ASD undergoing ablation for paroxysmal AF.ResultsBipolar voltage was significantly lower in ASD patients (right atrium: 1.53 ± 0.46 mV versus 1.98 ± 0.59 mV, P = 0.017; left atrium: 1.71 ± 0.36 mV versus 2.06 ± 0.63 mV, P = 0.039). There was no significant difference in global conduction velocity in either atrium between ASD and control patients. Effective refractory periods at 600 ms were not significantly different between patient groups (right atrium: 247 ± 34.7 ms versus 224 ± 36.5 ms, P = 0.071; left atrium: 244 ± 23.9 ms versus 232 ± 40.4 ms, P = 0.29). However, both conduction and repolarisation demonstrated greater rate adaptation in ASD patients in both atria.ConclusionRight atrial remodelling, characterised by atrial dilatation and increased low voltage, is present in ASD patients. During fixed rate pacing, conduction and repolarisation properties are similar between ASD and AF patients. However, the restitution properties of both conduction and repolarisation are more pronounced in ASD than AF patients.

Cardiac electrophysiology (CEP) simulations are increasingly used for understanding cardiac arrhythmias and guiding clinical decisions. However, these simulations typically require high-performance computing resources with numerous CPU cores, which are often inaccessible to many research groups and clinicians. To address this, we present TorchCor, a high-performance Python library for CEP simulations using the finite element method on general-purpose GPUs. Built on PyTorch, TorchCor significantly accelerates CEP simulations, particularly for large 3D meshes. The accuracy of the solver is verified against manufactured analytical solutions and the N-version benchmark problem. TorchCor is freely available for both academic and commercial use without restrictions.

BACKGROUND:Catheter ablation is an essential tool for ventricular arrhythmia management, yet sustained procedural success is hindered by the limited ability to identify nonendocardial arrhythmogenic substrates during the procedure. Although delayed enhancement cardiac magnetic resonance imaging is the reference standard for detecting myocardial fibrosis, barriers including cost, workflow complexity, and artifacts in patients with implantable devices limit its preprocedural use. We hypothesized that intracardiac electrograms provide sufficient information to infer scar beyond the endocardial surface and that this information can be harnessed by machine learning techniques. METHODS:This retrospective study included a total of 131 584 cardiac contact electrogram (EGM) signals collected from 46 patients undergoing ventricular arrhythmia ablation. A stratified patient-wise split was used to create the training/validation set (N=37) and the testing set (N=9), while ensuring a similar distribution of scar types. We developed a novel image-processing workflow to create scar labels using coregistered cardiac magnetic resonance imaging and electroanatomic mapping surface meshes. We developed a transformer-based self-supervised model, EGM2Scar-AI, alongside basic convolutional neural network models using either EGM waveforms or EGM-derived short-time Fourier transform spectrograms. RESULTS:The average task-specific area under the receiver operating characteristic curve of both the basic and short-time Fourier transform-based convolutional neural networks was 0.729 (0.725-0.732) and 0.729 (0.726-0.733), respectively, while EGM2Scar-AI performed significantly better with an average area under the receiver operating characteristic curve of 0.822 (0.819-0.825) across all 3 scar types. All models performed better on endocardial and mid-myocardial fibrosis identification, with a modest reduction in performance for epicardial fibrosis. Sensitivity improved significantly with the transformer-based architecture without appreciable changes in specificity. CONCLUSIONS:Our study demonstrates that routine intracardiac electrograms enable the identification of endocardial, mid-myocardial, and epicardial scar using a transformer-based self-supervised deep learning model. Ultimately, our model has the potential to provide magnetic resonance imaging-like fibrosis maps during EP procedures without the need for external imaging and to increase the data set size for patient-specific models in ventricular arrhythmia research.

Novel technologies and ablation techniques for identification of atrial fibrillation (AF) sources and personalized substrate modification may be required to improve outcomes for persistent AF. We hypothesize that a unison of electrophysiologic phase and optical flow mapping could be used to selectively prioritize ablation targets and optimize patient outcome while minimizing the tissue ablated. We aim to evaluate the efficacy of a novel electro-optic flow (EOF)-based ablation strategy for persistent AF patients using a virtual cohort of bi-atrial digital twins (DTs). A patient cohort (n=250) from a bi-atrial in silico population with different atrial fibrosis distributions was utilized to simulate five AF episodes per case. Phase singularity (PS) and average optical flow maps were computed for post-pulmonary vein isolation (PVI) sustained AF. Concordant regions, overlapping in at least three binarized PS maps were used to define regions to search for the highest optical curl cluster centroids as candidate EOF targets. Using optical curl as the weight, the centroid of five candidate EOF targets were computed and selected as an ablation target inside each concordant boundary. Six clinical ablation strategies were simulated. An inducibility-to-ablated tissue area metric was calculated to evaluate the efficacy of the tested ablation strategies. The pipeline automatically identified extra-PV targets and generated patient-specific EOF ablation plans. Electro-optic flow-guided ablations resulted in an average 32±2% AF inducibility, outperforming PVI (90±5%), and PVI+empiric (87±6%). Consensus-EOF further reduced inducibility to 20±5% while sparing 28±2% tissue as compared to PVI+PS ablation. Consensus mapping provides a novel method for assessing the dynamic nature of AF, while EOF offers a promising multimodal metric for identifying critical ablation targets outside of PVI. These findings underscore the potential of EOF-guided ablation planning in advancing the clinical translation of DT-based personalized therapy for PeAF patients.

Background:Myocardial strain imaging is a robust tool for evaluating extra-valvular remodelling in aortic stenosis (AS). Multi-phase cardiac computed tomography (CT) angiography acquired for transcatheter aortic valve implantation (TAVI) planning enables a novel three-dimensional (3D), geometry-independent strain assessment beyond two-dimensional (2D) transthoracic echocardiography (TTE). This study evaluates the agreement and reproducibility of CT- and TTE-derived longitudinal strain and examines its association with pulmonary hypertension (PH) in significant AS. Methods:Left ventricular global longitudinal strain (LV-GLS), left atrial global longitudinal reservoir strain (LA-LS), right ventricular global longitudinal strain (RV-GLS), and RV free-wall longitudinal strain (RV-FWLS) were determined using 2D TTE and CT-based 3D motion tracking in patients with severe AS undergoing TAVI evaluation. Patients with PH were defined using guideline-directed TTE criteria for high probability PH (H-PH: n = 43, 46.2%) and compared with those at low probability (L-PH: n = 50, 53.8%). Results:Agreement between CT and TTE was strong for LV-GLS (r = 0.837), RV-GLS (r = 0.853), and RV-FWLS (r = 0.780) and moderate for LA-LS (r = 0.677) (all p < 0.001). Peak longitudinal strain on both TTE and CT was significantly reduced in H-PH compared with L-PH (p < 0.001). Optimal strain cutoff values for identifying H-PH were lower on CT than on TTE (LV-GLS: -16.6% vs. -17.9%; LA-LS: 10.2% vs. 14.4%; RV-GLS: -15.3% vs. -20.1%; RV-FWLS: -18.0% vs. -21.1%). In an inter-modality comparison, it was found that TTE-derived LV-GLS was superior to CT-derived LV-GLS for detecting H-PH [AUC: 0.94 [95% CI 0.89-0.99] vs. 0.85 [95% CI 0.78-0.93], p = 0.013], whereas differences for LA-LS, RV-GLS, and RV-FWLS were non-significant (all p > 0.05). TTE- and CT-derived strain measurements showed excellent reproducibility (ICC > 0.9). Conclusion:TAVI CT is a promising tool for 3D longitudinal strain assessment and a valuable adjunct to TTE for quantifying extra-valvular remodelling associated with AS progression to PH. Further studies are warranted to evaluate the prognostic value of multi-chamber CT-derived 3D strain in AS.

Cardiac resynchronization therapy (CRT) guidelines are based on clinical trials with limited female representation and inconsistent left bundle branch block (LBBB) definitions. Conventional QRS duration (QRSd) criteria show variable diagnostic accuracy between sexes, partly due to differences in heart size and remodelling. We evaluated the influence of sex, heart size, LBBB, and conduction delay on QRSd and assessed the diagnostic performance of conventional and indexed QRSd criteria using a population-based modelling approach. Simulated QRSd were derived from electrophysiological simulations conducted in 2627 UK Biobank healthy participants and 359 patients with ischaemic heart disease, by modelling LBBB and normal activation combined with/without conduction delay. QRSd criteria under-selected LBBB females and over-selected non-LBBB patients. Indexing by LVEDV and LV mass reduced sex disparities but increased the over-selection in non-LBBB patients. Height-indexed QRSd effectively resolved sex differences and maintained low non-LBBB selection rates, demonstrating superior performance and potential for more equitable CRT selection. These results uncover a key mechanistic driver of sex differences in LBBB detection that may contribute to CRT outcomes and underscore the importance of incorporating sex-specific considerations into updated CRT guidelines to ensure equitable and effective treatment.

Cardiovascular diseases are the leading cause of death. Clinical data used to decide treatment are hard to integrate and interpret, making optimal treatment selection difficult. Personalized models can be used to integrate clinical data into a physics and physiology-constrained framework, but their clinical application faces limitations due to complex calibration and validation. In this study, we present a novel systematic calibration method for a whole-heart, multiscale, electromechanics model using emulators, sensitivity analysis, and history matching. Using cardiac motion derived from ECG-gated computed tomography (CT) and invasive left ventricular (LV) pressure data, we calibrated 25 model parameters to match the LV end-diastolic (ED) and peak pressure, ED and end-systolic (ES) volumes (EDV and ESV), right ventricle EDV, and the left atrium EDV, ESV, and the maximum volume during venous return. After calibration, all features were fit within [0.8, 10.8]% of the mean target value, and fell within 1.4 experimental standard deviations from the target values. We validated the model by comparing CT-derived and simulated atrioventricular plane displacement (AVPD) (8.2 versus 8.1 mm) and the ED and ES configurations against the CT images. The model replicated the measured acute hemodynamic response to biventricular (BIV) pacing (simulated: 222 mmHg/s versus clinical: 213±65 mmHg/s). This study provides a systematic method to integrate clinical data into a whole-heart, multiscale electromechanics framework. The validation shows that the model replicates local heart motion and response to therapy, demonstrating potential in assisting clinical decision-making.

Cardiac Magnetic Resonance (CMR) imaging is widely used to personalize heart models for cardiac digital twin analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hindering the capture of detailed anatomical structures. In this work, we introduce MorphiNet, a novel network that reproduces heart anatomy learned from high-resolution Computed Tomography (CT) images, unpaired with CMR images. MorphiNet encodes the anatomical structure as gradient fields, deforming template meshes into patient-specific geometries. A multilayer graph subdivision network refines these geometries while maintaining dense point correspondence, suitable for downstream computational analysis. MorphiNet achieved the strongest overall trade-off in bi-ventricular myocardium reconstruction on CMR patients with tetralogy of Fallot, with 0.3 higher Dice score and 2.6 lower Hausdorff distance compared to the best existing template-based methods, while achieving comparable geometric accuracy to neural implicit function methods on CT data at $50\times $ faster inference. Cross-dataset validation on the Automated Cardiac Diagnosis Challenge confirmed robust generalization, achieving a 0.7 Dice score with 30% improvement over previous template-based approaches. We validate our anatomical learning approach through the successful restoration of missing cardiac structures and demonstrate significant improvement over standard Loop subdivision. Motion tracking experiments further confirm MorphiNet's capability for cardiac function analysis, including ejection-fraction estimates that correctly identify myocardial dysfunction in tetralogy of Fallot patients. Code and checkpoints are available at https://github.com/MalikTeng/MorphiNetV2.

Background:The rise in cardiac implantable electronic devices implantation has resulted in a concomitant rise in transvenous lead extractions (TLEs). Advanced extraction tools such as the excimer laser and mechanical rotational sheaths have improved acute procedural success when manual extraction fails. However, it is unclear how the laser sheath compares with the rotational sheath in safety and efficacy outcomes. Objective:We aimed to compare the safety and efficacy of laser and rotational TLEs using a meta-analysis. Methods:A systematic literature search was performed for studies involving the use of laser and/or rotational sheath published from 2008 onward. A random-effects model was used to compare outcome data including complete procedural success, clinical success, major complications, minor complications, and procedural death. Results:43 studies were included for meta-analysis, consisting of 13,189 patients and 20,103 extracted leads. The overall mean lead dwell time was 8.5 ± 12.7 years. There was no significant difference between laser- and rotational-assisted TLEs in complete procedural success (94% vs 94%, respectively; P = .92), major complications (1.9% vs 0.81%, respectively; P = .10), minor complications (5% vs 4%, respectively; P = .74), and procedural death (0.2% vs 0.04%, respectively; P = .14). Laser TLE had a 5.6-fold aggregated risk of SVC laceration compared with rotational TLE but this was not associated with an increased mortality. Conclusion:Both laser and rotational TLEs are effective and safe. Our analysis suggests that there is no significant difference in safety profile between laser and rotational TLEs.

BACKGROUND:Left bundle branch block (LBBB) describes a specific cardiac conduction abnormality which may be a cause, consequence, or exacerbator of cardiovascular dysfunction and events. Electrocardiogram-derived definitions, which depend heavily on QRS duration (QRSd), are imperfect and continue to evolve, with limited data on predicting LBBB development. OBJECTIVES:This study aimed to determine whether changes in electrical axis identify patients at risk of future development of LBBB. METHODS:Retrospective data from 35,749 UK Biobank participants were analyzed, excluding those with overt cardiovascular disease. The primary endpoint was LBBB development. Associations with axis metrics (computed from baseline cardiac magnetic resonance imaging and 12-lead electrocardiogram) were investigated using Kaplan-Meier analysis and Cox proportional hazards models adjusted for age, sex, hypertension, left ventricular ejection fraction and QRSd. RESULTS:The cohort (age 63.4 ± 7.6 years, 45% male, QRSd: 87.4 ± 12.8 ms) was followed for a median of 6 years. Compared with the event-free population (N = 35,688), those who developed LBBB (N = 41) were older (69.2 vs 64.0 years; P < 0.001), more likely to have hypertension (31.7% vs 11.6%; P < 0.001), and had a significantly lower (more posterior) electrical axis (φElectrical: 69.1° vs 80.3°; P = 0.01). In multivariable analysis, lower φElectrical (HR: 0.73; 95% CI: 0.56-0.94; P = 0.014) was significantly associated with incident LBBB. Individuals with both high QRSd and low φElectrical had a four-fold increased risk (HR: 4.09; 95% CI: 1.84-8.99; P < 0.001). CONCLUSIONS:Posterior deviation of the transverse electrical axis complements QRSd in identifying individuals at risk of developing LBBB. These findings suggest that the electrical axis is capturing early conduction disease and may contribute to risk stratification of future development of LBBB.

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://aortaseg24.grand-challenge.org.

The human heart is a sophisticated system composed of four cardiac chambers with distinct shapes, which function in a coordinated manner. Existing shape models of the heart mainly focus on the ventricular chambers and they are derived from relatively small datasets. Here, we present a spatio-temporal (3D+t) statistical shape model of all four cardiac chambers, learnt from a large population of nearly 100,000 participants from the UK Biobank. A deep learning-based pipeline is developed to reconstruct 3D+t four-chamber meshes from the cardiac magnetic resonance images of the UK Biobank imaging population. Based on the reconstructed meshes, a 3D+t statistical shape model is learnt to characterise the shape variations and motion patterns of the four cardiac chambers. We reveal the associations of the four-chamber shape model with demographics, anthropometrics, cardiovascular risk factors, and cardiac diseases. Compared to conventional image-derived phenotypes, we validate that the four-chamber shape-derived phenotypes significantly enhance the performance in downstream tasks, including cardiovascular disease classification and heart age prediction. Furthermore, we demonstrate the effectiveness of shape-derived phenotypes in novel applications such as heart shape retrieval and heart re-identification from longitudinal data. To facilitate future research, we will release the learning-based mesh reconstruction pipeline, the four-chamber cardiac shape model, and return all derived four-chamber meshes to the UK Biobank.

BACKGROUND:Coronary revascularization is frequently undertaken to reduce ischemia in patients with ischemic left ventricular (LV) dysfunction. Whether revascularization modulates the substrate for ventricular arrhythmia is unclear. OBJECTIVE:The study aimed to assess the effects of revascularization on arrhythmic substrate in ischemic LV dysfunction and the association of the latter with changes in ischemia and scar. METHODS:Patients were enrolled if they had a LV ejection fraction (LVEF) ≤40%, extensive coronary disease (British Cardiovascular Intervention Society jeopardy score >6/12) and were scheduled to undergo percutaneous coronary intervention or coronary artery bypass surgery. Scar and ischemic burden were assessed via stress-perfusion cardiac magnetic resonance, calculated as a percentage of total LV myocardial volume. Arrhythmic substrate was characterized by non-invasive electrocardiographic imaging metrics, primarily LV activation recovery interval (ARI). Electrocardiographic imaging and perfusion cardiac magnetic resonance were repeated 3 months after revascularization. The primary outcome was change in LV ARI dispersion. RESULTS:Of 30 patients (age 67 ± 10 years, 87% male, LVEF 29 ± 7%), 12 (40%) underwent coronary artery bypass surgery, 18 (60%) had percutaneous coronary intervention. Following revascularization, LVEF increased (+8 ± 8%), ischemic burden reduced (-34 ± 24%), P < .01) and scar burden was unchanged. Mean LV ARI dispersion was unchanged; however, individual changes in LV ARI dispersion correlated with individual changes in ischemic burden (r = 0.51, P < .01). LV volumes and scar burden at baseline and change in indexed LV end-systolic volume and ischemia predicted improvement. CONCLUSION:Arrhythmic substrate correlated with scar burden and was unaltered by revascularization in this cohort. There was marked heterogeneity in residual ischemia which correlated with residual arrhythmic substrate. Further work is needed to personalize risk stratification in relation to ischemia reduction and residual arrhythmic risk.

Background: Electrocardiographic imaging maps cardiac electrical activity non-invasively but is restricted to the epicardium. Computational electrophysiology models can predict 3D activation and tissue properties but require extensive parameter calibration. Methods: We introduce an unbiased workflow combining sensitivity analysis with emulator-based Bayesian history matching to calibrate over 100 organ- and tissue-scale parameters. The framework incorporates CT-scan images and 12-lead ECGs with a multi-scale electrophysiology model to generate personalised ventricular simulations. Results: The framework was tested on seven subjects (four with synthetic and three with clinical ECGs), with validation performed using high-density body surface potentials from a 252-electrode vest for the clinical cases. Calibrated models reproduced individual ECG morphologies and showed strong agreement with independent measurements (Pearson's correlation coefficient: 0.80±0.04). Conclusions: The study links non-invasive data with high-fidelity simulations to estimate spatially-varying properties, supporting personalised cardiac modelling for clinical use.

Background Conduction system pacing with leadless left bundle branch area pacing (LBBAP) is a promising application of leadless pacemakers (L-PMs). However, the mechanical impact of the L-PM on intracardiac structures may worsen tricuspid regurgitation and induce ventricular arrhythmia. The optimal site of leadless conduction system pacing also remains unclear. Objective This study aimed to quantify device- and site-specific collision risks and identify the optimal site for leadless LBBAP using computational modeling. Methods We conducted a modeling study assessing collision risks of contemporary (Micra TPS, Aveir AR, and Aveir VR) and future L-PM devices and exploring the optimal site for leadless LBBAP using cardiac computed tomography models from 10 patients with heart failure. Virtual L-PM implantation on the right ventricular (RV) septal wall was performed, and models of the RV free wall, papillary muscles, moderator band, and tricuspid valve structures enabled assessment of device-structure interactions. Computer simulations examined collision risk across devices of varying dimensions throughout the cardiac cycle. Results Collision risk increased with device length and volume, primarily driven by RV wall interactions. Apical pacing sites carried a 73.6%-75.2% collision risk, and overall wall collisions increased by 3%-8% per 5 mm device length. Tricuspid valve and papillary muscle collisions depended on implant region, with the highest risks of collision in basal-inferoseptal (>70%) and mid-inferoseptal regions (>40%), respectively. Leadless LBBAP via the left anterior fascicle had the lowest collision rates. Conclusion These findings support patient-specific device selection and careful septal targeting of leadless LBBAP to minimize complications, optimize physiological activation, and guide future L-PM designs.

There is growing motivation to exploit computational biomechanical modeling of the heart as a predictive tool to support clinical diagnoses and therapies. Existing patient-specific cardiac models often rely on data collected under highly standardized conditions in hospitals. However, disease progression and therapy responses often depend on stressors, encountered in daily life, that cannot be captured in a traditional clinical setting. To achieve clinical translation, existing modeling frameworks must be refined and extended to include such influences. The "digital twin" concept, in which models of specific systems are continually updated with new data, is a promising avenue for integrating and interpreting these data streams. However, this endeavor calls for novel approaches to model development and data acquisition and integration. We review modeling approaches addressing specific stressor types (caffeine, exercise, sex-dependent factors, sleep, the environment) to identify knowledge gaps, assess emerging technical challenges, and suggest potential model developments to extend the scope and reach of biomedical cardiac simulations.

Non-responders to conventional coronary sinus CRT represent a high-risk heart failure population with limited treatment options. Leadless LV endocardial pacing may provide benefit by delivering more physiological activation patterns, and enabling pacing from optimized LV cavity sites. We aimed to evaluate the efficacy of leadless endocardial pacing using the WiSE-CRT system in CRT non-responders. A pooled patient-level post-hoc analysis of the SELECT-LV study, the WiSE Post-Market Registry and the SOLVE-CRT trial was performed. Multivariable regression analysis identified predictors of response to WiSE-CRT, followed by ROC analysis and subgroup analysis by degree of QRS narrowing. The total cohort comprised of 71 patients. Complete echocardiographic data was available in 66 patients, and complete ECG data available in 45 patients. The clinical response rate (improvement in at least one NYHA class) was 46

Atrial fibrillation (AF) is the most common arrhythmia, with few treatment options. To discover novel pathways, we performed mass spectrometry (MS) on atrial tissue from patients in Sinus Rhythm or with AF without heart failure. We identified changes in canonical AF pathways, although surprisingly, contractile proteins and specifically a loss of atrial isoforms. Functional remodeling was confirmed in AF cardiomyocytes, revealing increased contractility compared to SR. We performed MS analysis of human atrial and ventricular tissue and found that ~1/3 of proteomic remodeling in AF was associated with chamber identity. Using atrial hiPSC-CM Engineered Heart Tissues to model AF, we replicated proteomic and contractile remodeling observed in human tissue, indicating mechano-sensing likely drives these effects. Lastly, an integrative patient simulation suggests this cellular remodeling is likely maladaptive. Together, these results reveal a novel role for sarcomere remodeling and a loss of atrial identity in AF, representing potential new therapeutic targets.

Introduction:Non-physiological right ventricular pacing (RVP) is currently the mainstay of treatment for patients with high-degree atrioventricular (AV) block who have preserved left ventricular ejection fraction. Newer pacing strategies, such as left bundle branch pacing (LBBP) and leadless cardiac pacemakers (LCPMs), are increasingly being adopted due to their respective advantages over RVP. However, there has been no direct comparison between LCPMs and LBBP regarding their risk of pacing-induced cardiomyopathy, which is thought to arise from interventricular and intraventricular dyssynchrony. Using in silico modelling, we compared the effects of LBBP and LCPMs on interventricular and intraventricular synchrony. Methods:Using 19 four-chamber healthy heart geometries, we simulated LCPMs at the level of the right ventricular outflow tract-septum (RVOT-S), mid-septum (MS), and apical septum (AS), along with proximal left bundle pacing (PLBBP) and distal left bundle pacing (DLBBP) in 3 different settings: 1) intact left bundle branch conduction, 2) left bundle branch block (LBBB), and 3) septal scar involving the His-Purkinje system (HPS). Ventricular electrical uncoupling (VEU), absolute VEU, and left ventricular dyssynchrony index (LVDI) were measured. The shortest interval required to activate 90% of both ventricles (BIVAT-90) was also recorded. Results:In the setting of intact left bundle branch conduction, combined LBBP configurations had significantly lower VEU (LBBP: -3.3 ± 5.1 vs. LCPM: 24.2 ± 7.6 ms, p < 0.01) and absolute VEU (LBBP: 5.0 ± 3.5 vs. LCPM: 24.2 ± 7.6 ms, p < 0.01) than combined LCPM configurations. In the presence of proximal LBBB, combined LBBP configurations also had significantly lower VEU (LBBP -22.1 ± 0.5 vs. LCPM 25.9 ± 7.9, p < 0.01) and absolute VEU (LBBP 22.1 ± 0.5 vs. LCPM 25.9 ± 7.9 ms, p < 0.01) than combined LCPM configurations. However, there was no significant difference in absolute VEU when combined LBBP configurations was compared with RVOT-S configuration alone (LBBP 22.1 ± 0.5 vs. RVOT-S 21.7 ± 9.0 ms, p = 0.86). In the presence of septal scar, combined LCPM configurations had significantly lower VEU compared with combined LBBP configurations (VEU: LCPM 31.0 ± 8.4 vs. LBBP 41.7 ± 20.2 ms, respectively; p < 0.01). Combined LBBP configurations had significantly lower LVDI and BIVAT-90 compared with combined LCPM configurations in both the presence and absence of LBBB, but there was no significant difference between the two in the setting of a septal scar. Conclusion:LCPM produces less interventricular dyssynchrony than LBBP in the presence of extensive septal scarring involving the HPS. In the setting of proximal LBBB, LCPM at the RVOT-S level may be non-inferior to LBBP in terms of interventricular dyssynchrony.

Background Cardiac resynchronization therapy (CRT) delivered with left ventricular (LV) epicardial pacing may increase arrhythmic risk through detrimental effects on ventricular repolarization. Leadless LV endocardial CRT including leadless left bundle branch area pacing (LBBAP) may mitigate this by preserving a more physiological transmural activation pattern. Objective This study aimed to evaluate the effect of leadless LV endocardial and leadless LBBAP on repolarization metrics derived from electrocardiographic imaging (ECGi). Methods Ten patients with leadless endocardial CRT systems underwent a temporary pacing plus ECGi study, testing right ventricular, LV, and biventricular pacing (BiVP) settings, as well as atrioventricular-optimized LV pacing. Epicardial electrograms were used to derive metrics of repolarization and activation-recovery interval dispersion. The primary outcome measurements were acute improvement (ie, reduction) from baseline (right ventricular pacing or underlying rhythm) of these repolarization metrics. Results Ten patients were studied; 5 had received LV lateral wall endocardial pacing, and 5 had received LBBAP with leadless septal wall pacing. The optimal leadless pacing setting significantly improved biventricular dispersion of repolarization by 23.7% ± 14% (P < .01), and this effect was more pronounced with LBBAP (29.3% ± 15%, P = .01) vs lateral wall pacing (18% ± 12%, P = .03). Similar results were observed for activation-recovery interval dispersion and biventricular repolarization gradients. The most pronounced improvements were observed where LV-only pacing as opposed to BiVP was used, either through LBBAP or atrioventricular-optimized LV pacing from any endocardial location. Conclusion Optimized leadless LV endocardial lateral wall pacing and LBBAP improve ECGi-derived ventricular repolarization metrics. LV-only pacing seemed superior to endocardial BiVP, potentially reflecting repolarization heterogeneity caused by a collision of 2 paced wavefronts.