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In certain cardiac conduction system pathologies, like bundle branch block, block may be proximal, allowing for electrical stimulation of the more distal His bundle to most effectively restore activation. While selective stimulation of the His bundle is sought, surrounding myocardium may also be excited, resulting in nonselective pacing. The myocardium and His bundle have distinct capture thresholds, but the factors affecting whether His bundle pacing is selective or nonselective remain unelucidated. Objective: We investigated the properties which affect the capture thresholds in order to improve selective pacing. Methods: We performed biophysically detailed, computer simulations of a His fibre running through a septal wedge preparation to compute capture thresholds under various configurations of electrode polarity and orientation. Results: The myocardial capture threshold was close to that of the His bundle. The His fibre needed to intersect with the electrode tip to favor its activation. Inserting the electrode fully within the septum increased the myocardial capture threshold. Reversing polarity, to rely on anode break excitation, also increased the ease of selective pacing. Conclusion: Model results were consistent with clinical observations. For selective pacing, the tip needs to be in contact with the His fibre and anodal stimulation is preferable. Significance: This study provides insight into helping establish electrode and stimulation parameters for selective His bundle pacing in patients.

Personalized models of cardiac electrophysiology (EP) that match clinical observation with high fidelity, referred to as cardiac digital twins (CDTs), show promise as a tool for tailoring cardiac precision therapies. Building CDTs of cardiac EP relies on the ability of models to replicate the ventricular activation sequence under a broad range of conditions. Of pivotal importance is the His–Purkinje system (HPS) within the ventricles. Workflows for the generation and incorporation of HPS models are needed for use in cardiac digital twinning pipelines that aim to minimize the misfit between model predictions and clinical data such as the 12 lead electrocardiogram (ECG). We thus develop an automated two stage approach for HPS personalization. A fascicular-based model is first introduced that modulates the endocardial Purkinje network. Only emergent features of sites of earliest activation within the ventricular myocardium and a fast-conducting sub-endocardial layer are accounted for. It is then replaced by a topologically realistic Purkinje-based representation of the HPS. Feasibility of the approach is demonstrated. Equivalence between both HPS model representations is investigated by comparing activation patterns and 12 lead ECGs under both sinus rhythm and right-ventricular apical pacing. Predominant ECG morphology is preserved by both HPS models under sinus conditions, but elucidates differences during pacing. Supplementary Information The online version contains supplementary material available at 10.1007/s10439-021-02825-9.

Background Epicardial pacing increases risk of ventricular tachycardia (VT) in patients with ischemic cardiomyopathy (ICM) when pacing in proximity to scar. Endocardial pacing may be less arrhythmogenic as it preserves the physiological sequences of activation and repolarization. Objective The purpose of this study was to determine the relative arrhythmogenic risk of endocardial compared to epicardial pacing, and the role of the transmural gradient of action potential duration (APD) and pacing location relative to scar on arrhythmogenic risk during endocardial pacing. Methods Computational models of ICM patients (n = 24) were used to simulate left ventricular (LV) epicardial and endocardial pacing 0.2–3.5 cm from a scar. Mechanisms were investigated in idealized models of the ventricular wall and scar. Simulations were run with/without a 20-ms transmural APD gradient in the physiological direction and with the gradient inverted. Dispersion of repolarization was computed as a surrogate of VT risk. Results Patient-specific models with a physiological APD gradient predict that endocardial pacing decreases VT risk (34%; P

Background Computational models of the heart built from cardiac MRI and electrophysiology (EP) data have shown promise for predicting the risk of and ablation targets for myocardial infarction (MI) related ventricular tachycardia (VT), as well as to predict paced activation sequences in heart failure patients. However, most recent studies have relied on low resolution imaging data and little or no EP personalisation, which may affect the accuracy of model-based predictions. Objective To investigate the impact of model anatomy, MI scar morphology, and EP personalisation strategies on paced activation sequences and VT inducibility to determine the level of detail required to make accurate model-based predictions. Methods Imaging and EP data were acquired from a cohort of six pigs with experimentally induced MI. Computational models of ventricular anatomy, incorporating MI scar, were constructed including bi-ventricular or left ventricular (LV) only anatomy, and MI scar morphology with varying detail. Tissue conductivities and action potential duration (APD) were fitted to 12-lead ECG data using the QRS duration and the QT interval, respectively, in addition to corresponding literature parameters. Paced activation sequences and VT induction were simulated. Simulated paced activation and VT inducibility were compared between models and against experimental data. Results Simulations predict that the level of model anatomical detail has little effect on simulated paced activation, with all model predictions comparing closely with invasive EP measurements. However, detailed scar morphology from high-resolution images, bi-ventricular anatomy, and personalized tissue conductivities are required for predict experimental VT outcome. Conclusion This study provides clear guidance for model generation based on clinical data. While a representing high level of anatomical and scar detail will require high-resolution image acquisition, EP personalisation based on 12-lead ECG can be readily incorporated into modelling pipelines, as such data is widely available.

Cardiac output is dependent on the tight coupling between atrial and ventricular function. The study of such interaction mechanisms is hindered by their complexity, and therefore requires a systematic approach. We have developed a four-chamber closed-loop cardiac electromechanics model which, through the coupling of the chambers with a closed-loop cardiovascular system model and the effect of the pericardium, is able to capture atrioventricular interaction. Our model simulates electrical activation and contraction of the atria and the ventricles coupled with a closed-loop model based on the CircAdapt framework. We include the effect of the pericardium on the heart using normal springs, scaling the local spring stiffness based on image-derived motion. The coupled model was used to study the impact of ventricular myofibre orientation on atrial dynamics by varying ventricular fibre orientation from –40\(^\circ \)/+40\(^\circ \) to –70\(^\circ \)/+70\(^\circ \). We found that steeper fibres increase atrioventricular valve plane motion from 1.0 mm to 14.0 mm, leading to a lower minimum left atrial (LA) pressure (–0.4 mmHg vs –1.1 mmHg) and greater venous return (LA maximum volume: 168 mL vs 182 mL), and that fibres angles –50\(^\circ \)/+50\(^\circ \) were consistent with a physiological atrial contraction and filling pattern. Our framework is capable of capturing complex interaction dynamics between the atria, the ventricles and the circulatory system accounting for the effect of the pericardium. Such simulation platform represents a useful tool to study both systolic and filling phases of all cardiac chambers, and how these get altered in diseased states and in response to treatment.

His bundle pacing (HBP) and left bundle pacing (LBP) are novel delivery methods for cardiac resynchronisation therapy (CRT) for left bundle branch block (LBBB) patients. Septal flash (SF), an abnormal pre-ejection motion of the septum towards the left ventricle (LV) arising from dyssynchronous activation, has been shown in the past to be a robust and independent predictor for CRT response. Although small-cohort studies showed the feasibility and efficacy of HBP and LBP, the effects of HBP and LBP on septal motion have yet to be investigated. In this study, we used our four-chamber heart electro-mechanics simulation framework to determine whether HBP and LBP can correct for SF. We performed simulations in four four-chamber heart models. In synchronous and LBBB activation, simulated mean lateral septal movement from the right ventricle (RV) into the LV was -0.4±0.5mm and - 3.7±0.9mm (p

Funding Acknowledgements WT 203148/Z/16/Z; MR/N011007/1; RE/08/003; PG/15/91/31812; PG/16/81/32441 Background Endocardial pacing has been shown to improve response to cardiac resynchronization therapy (CRT) in comparison to conventional epicardial pacing and the physiological activation, endocardium to epicardium, is proposed to make it less arrhythmogenic. However, the relative arrhythmic risk of endocardial and epicardial pacing has not been systematically investigated. Pacing in proximity to scar increases susceptibility to arrhythmogenesis during epicardial pacing. Whether this is also the case during endocardial pacing is currently unknown. Purpose We investigate 1) whether endocardial pacing is less arrhythmogenic than epicardial pacing, 2) whether pacing location relative to scar plays a role in arrhythmogenesis during endocardial pacing, and 3) whether these findings could be explained by the direction of the transmural action potential duration (APD) gradient. Methods We used computational models of ischemic heart failure and patient-specific (n = 24) left ventricular anatomy and scar morphology to simulate repolarization during endocardial and epicardial pacing. Pacing locations were selected 0.2-3.5cm from a scar. We ran simulations with a 20ms transmural APD gradient, as found in heart failure, from the epicardium to endocardium (physiological) and with this gradient inverted. We computed the volume of high (>3ms/mm) repolarization gradients (HRG) within 1cm around a scar, as a surrogate for arrhythmia risk, and analysed these with ANOVA and Tukey-Kramer post-hoc tests. Results Simulations with a physiological APD gradient predict that endocardial pacing creates a smaller (34%) volume of HRG around (1cm) a scar compared to epicardial pacing when pacing 0.2cm from scar (Figure 1-A). The volume of HRG decreases (P from scar for epicardial pacing but not endocardial pacing (Figure 1-A). Inverting the transmural APD gradient, inverts the trend observed with a physiological gradient. In this case, the volume of HRG is unaffected by pacing location during epicardial pacing, whereas it decreases (19%) with the distance from scar for endocardial pacing. This is illustrated in the regions highlighted in yellow in Figure 1 for endocardial pacing at 0.2 and 3.5cm from a scar with a physiological (B) and an inverted (C) gradient. Conclusions Endocardial pacing is less arrhythmogenic (purpose 1) than conventional epicardial pacing when pacing in proximity to scar and is also less susceptible to pacing location relative to scar (purpose 2). The direction of the transmural APD gradient offers a mechanistic explanation for reduced susceptibility to arrhythmogenesis during endocardial pacing compared to epicardial pacing (purpose 3). Endocardial pacing is an attractive alternative to conventional epicardial pacing in patients with scar, as it allows pacing in proximity to scar while avoiding increasing arrhythmogenic risk in patients with ischemic heart failure. Abstract Figure.

His-bundle pacing (HBP) and left bundle pacing (LBP) are emerging as novel delivery methods for cardiac resynchronization therapy (CRT) in heart failure patients with left bundle branch block (LBBB). HBP and LBP have never been compared to biventricular endocardial (BiV-endo) pacing. Furthermore, there are indications of negative effects of LBP on right ventricular (RV) activation times (ATs), but these effects have not been quantified.The purpose of this study was to compare changes in ventricular activation induced by HBP, LBP, left ventricular (LV) septal pacing, BiV-endo, and biventricular epicardial (BiV-epi) pacing using computer simulations.We simulated ventricular activation on 24 four-chamber heart meshes inclusive of the His-Purkinje network in the presence of LBBB. We simulated BiV-epi pacing, BiV-endo pacing with left ventricular (LV) lead at the lateral wall, BiV-endo pacing with LV lead at the LV septum, HBP, and LBP.HBP was superior to BiV-endo and BiV-epi in terms of reduction in LV ATs and interventricular dyssynchrony (P.05). LBP reduced LV ATs but not interventricular dyssynchrony compared to BiV-epi and BiV-endo pacing. RV latest AT was higher with LBP than with HBP (141.3 ± 10.0 ms vs 111.8 ± 10.4 ms). Optimizing AV delay during LBP reduced RV latest AT (104.7 ± 8.7 ms) and led to comparable response to HBP. In case of complete AV block, BiV-endo septal pacing was equivalent to LBP.HBP is superior to BiV-epi and BiV-endo. To achieve comparable response to HBP, AV delay optimization during LBP is required in order to reduce RV ATs.

Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 6714 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.10.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60 and 80 at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.390.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 14916ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 351% and 302%, respectively, and a LV and RV stroke volume (SV) of 9528mL and 6511mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications. (VLID)5215097 Version of record

The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. The anatomy plays an important role in determining local wall stress; however, the relative contribution of wall thickness and curvature in determining wall stress in the LA is unknown. We have developed electromechanical finite element (FE) models of the LA using patient-specific anatomical FE meshes with rule-based myofiber directions. The models of the LA were passively inflated to 10mmHg followed by simulation of the contraction phase of the atrial cardiac cycle. The FE models predicted maximum LA volumes of 156.5 mL, 99.3 mL and 83.4 mL and ejection fractions of 36.9%, 32.0% and 25.2%. The median wall thickness in the 3 cases was calculated as $$1.32\, \pm \,0.78$$1.32±0.78 mm, $$1.21\, \pm \,0.85$$1.21±0.85 mm, and $$0.74\,\pm \,0.34$$0.74±0.34 mm. The median curvature was determined as $$0.159\,\pm \,0.080$$0.159±0.080 $$\hbox {mm}^{-1}$$mm-1, $$0.165\,\pm \,0.079\,\hbox {mm}^{-1}$$0.165±0.079mm-1, and $$0.166\,\pm \,0.077\,\hbox {mm}^{-1}$$0.166±0.077mm-1. Following passive inflation, the correlation of wall stress with the inverse of wall thickness and curvature was 0.55–0.62 and 0.20–0.25, respectively. At peak contraction, the correlation of wall stress with the inverse of wall thickness and curvature was 0.38–0.44 and 0.16–0.34, respectively. In the LA, the 1st principal Cauchy stress is more dependent on wall thickness than curvature during passive inflation and both correlations decrease during active contraction. This emphasizes the importance of including the heterogeneous wall thickness in electromechanical FE simulations of the LA. Overall, simulation results and sensitivity analyses show that in complex atrial anatomy it is unlikely that a simple anatomical-based law can be used to estimate local wall stress, demonstrating the importance of FE analyses.

Introduction: Personalized in silico models of cardiac electrophysiology based on non-invasive recordings, such as body surface potential maps, are considered of pivotal importance in clinical modeling applications. Efficient, automated workflows are desired to construct patientspecific models for clinical use. Objective: We aimed to develop an automated workflow for the generation of a parameterizable cardiac EP model capable of simulating body surface potential maps independent of user interaction. Methods: A cardiac bi-ventricular model with torso was segmented and meshed from clinical MRI scans. Universal ventricular coordinates were computed for userindependent definition of fibers, a fast conducting endocardial layer, and earliest activation on the endocardium. The extracellular epicardial potential distribution was simulated and projected to the torso surface to acquire a body surface potential map. Results: Total model generation from segmentation required approximately 2 hours. Automatized simulation of a single depolarization sequence required approximately 30 minutes using a forward element method implementation. Discussion: The proposed workflow integrated recentlydeveloped technologies to generate a parameterizable cardiac EP model within clinical time scales.

Cardiac Resynchronization Therapy (CRT) is associated with increased arrhythmogenic risk in infarct patients when pacing adjacent to a scar. We investigated the role of pacing location relative to scar on dispersion of repolarization, as a surrogate for arrhythmogenic risk. For this task, we developed a personalization and simulation pipeline that allows fast development of personalized computational models and simulation of cardiac electrophysiology. Twenty four models of left ventricular anatomy and scar morphology were built and repolarization sequences were simulated. Simulation results show that CRT increases dispersion of repolarization around a scar when pacing adjacent to it, thus, providing a mechanistic explanation of increased arrhythmogenic risk in infarct patients undergoing CRT.

Implanted cardiac defibrillators (ICDs) seek to automatically detect and terminate potentially lethal ventricular arrhythmias by applying strong internal electric shocks across the heart. However, the optimisation of the specific electrode design and configurations represents an intensive area of research in the pursuit of reduced shock strengths and fewer device complications and risks. Computational whole-torso simulations play an important role in this endeavour, although knowing which specific metric should be used to assess configuration efficacy and assessing the impact of different patient anatomies and pathologies, and the corresponding effect this may have on different metrics has not been investigated. We constructed a cohort of CT-derived high-resolution whole torso-cardiac computational models, including variants of cardiomyopathies and patients with differing torso dimensions. Simulations of electric shock application between electrode configurations corresponding to transveneous (TV-ICD) and subcutaneous (S-ICD) ICDs were modelled and conventional metrics such as defibrillation threshold (DFT) and impedance computed. In addition, we computed a novel metric termed the shock vector efficiency (η), which quantifies the fraction of electrical energy dissipated in the heart relative to the rest of the torso. Across the cohort, S-ICD configurations showed higher DFTs and impedances than TV-ICDs, as expected, although little consistent difference was seen between healthy and cardiomyopathy variants. η was consistently, Highlights • Computational modelling plays an important role in optimising ICD configurations. • Our novel metric quantifies the relative energy used by the device to defibrillate. • It provides useful information, in conjunction with DFT and device impedance.