Predicting Post-Infarct Ventricular Tachycardia by Integrating Cardiac MRI and Advanced Computational Reentrant Pathway Analysis

BACKGROUND

Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death (SCD) after a myocardial infarction. However, improved risk stratification for device requirement is still needed.

OBJECTIVE

To improve assessment of post-infarct ventricular electro-pathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modelling.

METHODS

ADAS LV and custom-made software was used to generate 3D patient-specific ventricular models in a prospective cohort of post-infarct patients (n=40) having undergone LGE imaging pre-ICD implantation. Corridor metrics and 3D surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced VTs and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up.

RESULTS

Patients with an event (n=17) had a larger interface between healthy-scar and higher VITA metrics. Cox-regression demonstrated a significant independent association with an event: interface (HR 2.79; 1.44-5.44, p < .01), unique VTs (HR 1.67; CI 1.04-2.68, p = .03), mean RTT (HR 2.14; CI 1.11-4.12, p = .02), maximum RTT (HR 2.13; CI 1.19-3.81, p = .01).

CONCLUSION

Detailed quantitative analysis of LGE based scarmaps, combined with advanced computational modeling, is able to accurately predict ICD therapy and could facilitate early identification of high-risk patients in addition to LVEF.

Digital twins for cardiac electrophysiology: state of the art and future challenges

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.

Neural network emulation of the human ventricular cardiomyocyte action potential for more efficient computations in pharmacological studies

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47 mV in normal APs and of 14.5 mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.22 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrained in-silico representations, enabling inference of multi-scale properties tied to these mechanisms. We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework. We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes. Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.

A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.

Neural network emulation of the human ventricular cardiomyocyte action potential: a tool for more efficient computation in pharmacological studies

Computer models of the human ventricular cardiomyocyte action potential (AP) have reached a level of detail and maturity that has led to an increasing number of applications in the pharmaceutical sector. However, interfacing the models with experimental data can become a significant computational burden. To mitigate the computational burden, the present study introduces a neural network (NN) that emulates the AP for given maximum conductances of selected ion channels, pumps, and exchangers. Its applicability in pharmacological studies was tested on synthetic and experimental data. The NN emulator potentially enables massive speed-ups compared to regular simulations and the forward problem (find drugged AP for pharmacological parameters defined as scaling factors of control maximum conductances) on synthetic data could be solved with average root-mean-square errors (RMSE) of 0.47mV in normal APs and of 14.5mV in abnormal APs exhibiting early afterdepolarizations (72.5% of the emulated APs were alining with the abnormality, and the substantial majority of the remaining APs demonstrated pronounced proximity). This demonstrates not only very fast and mostly very accurate AP emulations but also the capability of accounting for discontinuities, a major advantage over existing emulation strategies. Furthermore, the inverse problem (find pharmacological parameters for control and drugged APs through optimization) on synthetic data could be solved with high accuracy shown by a maximum RMSE of 0.21 in the estimated pharmacological parameters. However, notable mismatches were observed between pharmacological parameters estimated from experimental data and distributions obtained from the Comprehensive in vitro Proarrhythmia Assay initiative. This reveals larger inaccuracies which can be attributed particularly to the fact that small tissue preparations were studied while the emulator was trained on single cardiomyocyte data. Overall, our study highlights the potential of NN emulators as powerful tool for an increased efficiency in future quantitative systems pharmacology studies.

Constructing bilayer and volumetric atrial models at scale

To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).

Guided implantation of a leadless left ventricular endocardial electrode and acoustic transmitter using computed tomography anatomy, dynamic perfusion and mechanics, and predicted activation pattern

BACKGROUND

The WiSE-CRT System (EBR systems, Sunnyvale, CA) permits leadless left ventricular pacing. Currently, no intraprocedural guidance is used to target optimal electrode placement while simultaneously guiding acoustic transmitter placement in close proximity to the electrode to ensure adequate power delivery.

OBJECTIVE

The purpose of this study was to assess the use of computed tomography (CT) anatomy, dynamic perfusion and mechanics, and predicted activation pattern to identify both the optimal electrode and transmitter locations.

METHODS

A novel CT protocol was developed using preprocedural imaging and simulation to identify target segments (TSs) for electrode implantation, with late electrical and mechanical activation, with ≥5 mm wall thickness without perfusion defects. Modeling of the acoustic intensity from different transmitter implantation sites to the TSs was used to identify the optimal transmitter location. During implantation, TSs were overlaid on fluoroscopy to guide optimal electrode location that were evaluated by acute hemodynamic response (AHR) by measuring the maximal rate of left ventricular pressure rise with biventricular pacing.

RESULTS

Ten patients underwent the implantation procedure. The transmitter could be implanted within the recommended site on the basis of preprocedural analysis in all patients. CT identified a mean of 4.8 ± 3.5 segments per patient with wall thickness < 5 mm. During electrode implantation, biventricular pacing within TSs resulted in a significant improvement in AHR vs non-TSs (25.5% ± 8.8% vs 12.9% ± 8.6%; P < .001). Pacing in CT-identified scar resulted in either failure to capture or minimal AHR improvement. The electrode was targeted to the TSs in all patients and was implanted in the TSs in 80%.

CONCLUSION

Preprocedural imaging and modeling data with intraprocedural guidance can successfully guide WiSE-CRT electrode and transmitter implantation to allow optimal AHR and adequate power delivery.

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling

AIMS

Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility.

METHODS

Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up.

RESULTS

Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence.

CONCLUSION

Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.

Frequency Domain Analysis of Endocardial Electrograms for Detection of Nontransmural Myocardial Fibrosis in Nonischemic Cardiomyopathy

BACKGROUND

Voltage mapping in nonischemic cardiomyopathy can fail to identify midmyocardial substrate for ventricular arrhythmias, an important cause of ablation failure.

OBJECTIVES

The aim of this study was to assess whether frequency domain analysis of endocardial left ventricular electrograms (EGMs) can better predict the presence of midmyocardial fibrosis (MMF) compared with voltage amplitude.

METHODS

Nonischemic cardiomyopathy patients undergoing ventricular tachycardia ablation with registered preprocedural cardiac computed tomography and late iodine enhancement were included. Presence of fibrosis at each EGM site was assessed. Bipolar and unipolar EGMs were transformed to the frequency domain using multitaper spectral analysis. Singular value decomposition of the EGM frequency spectrum was used within a supervised machine learning process to select features to predict the presence of MMF and compare against predictions using voltage amplitude.

RESULTS

Thirteen patients were included (median age 57 years [IQR: 28-73 years], median ejection fraction 40% [IQR: 15%-57%]). A total of 6,015 EGM pairs were processed: 2,459 EGM pairs in MMF areas and 3,556 EGM pairs in non-MMF areas. Supervised classifiers were trained with stratified k-fold cross-validation within patients. The distribution of mean area under the curve metrics using frequency features, f, was significantly greater than voltage feature area under the curve metrics, v, (mean f = 0.841 [95% CI: 0.789-0.884] vs mean v = 0.591 [95% CI: 0.530-0.658]; P < 0.001), indicating that frequency-trained classifiers better predicted the presence of MMF.

CONCLUSIONS

These data indicate the promising discriminatory value of endocardial EGM frequency content in the assessment of concealed myocardial substrate. Further studies are needed to investigate the importance of the specific frequency features identified.

Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators

Cardiac pump function arises from a series of highly orchestrated events across multiple scales. Computational electromechanics can encode these events in physics-constrained models. However, the large number of parameters in these models has made the systematic study of the link between cellular, tissue, and organ scale parameters to whole heart physiology challenging. A patient-specific anatomical heart model, or digital twin, was created. Cellular ionic dynamics and contraction were simulated with the Courtemanche-Land and the ToR-ORd-Land models for the atria and the ventricles, respectively. Whole heart contraction was coupled with the circulatory system, simulated with CircAdapt, while accounting for the effect of the pericardium on cardiac motion. The four-chamber electromechanics framework resulted in 117 parameters of interest. The model was broken into five hierarchical sub-models: tissue electrophysiology, ToR-ORd-Land model, Courtemanche-Land model, passive mechanics and CircAdapt. For each sub-model, we trained Gaussian processes emulators (GPEs) that were then used to perform a global sensitivity analysis (GSA) to retain parameters explaining 90% of the total sensitivity for subsequent analysis. We identified 45 out of 117 parameters that were important for whole heart function. We performed a GSA over these 45 parameters and identified the systemic and pulmonary peripheral resistance as being critical parameters for a wide range of volumetric and hemodynamic cardiac indexes across all four chambers. We have shown that GPEs provide a robust method for mapping between cellular properties and clinical measurements. This could be applied to identify parameters that can be calibrated in patient-specific models or digital twins, and to link cellular function to clinical indexes.

Effect of scar and His-Purkinje and myocardium conduction on response to conduction system pacing

INTRODUCTION

Conduction system pacing (CSP), in the form of His bundle pacing (HBP) or left bundle branch pacing (LBBP), is emerging as a valuable cardiac resynchronization therapy (CRT) delivery method. However, patient selection and therapy personalization for CSP delivery remain poorly characterized. We aim to compare pacing-induced electrical synchrony during CRT, HBP, LBBP, HBP with left ventricular (LV) epicardial lead (His-optimized CRT [HOT-CRT]), and LBBP with LV epicardial lead (LBBP-optimized CRT [LOT-CRT]) in patients with different conduction disease presentations using computational modeling.

METHODS

We simulated ventricular activation on 24 four-chamber heart geometries, including His-Purkinje systems with proximal left bundle branch block (LBBB). We simulated septal scar, LV lateral wall scar, and mild and severe myocardium and LV His-Purkinje system conduction disease by decreasing the conduction velocity (CV) down to 70% and 35% of the healthy CV. Electrical synchrony was measured by the shortest interval to activate 90% of the ventricles (90% of biventricular activation time [BIVAT-90]).

RESULTS

Severe LV His-Purkinje conduction disease favored CRT (BIVAT-90: HBP 101.5 ± 7.8 ms vs. CRT 93.0 ± 8.9 ms, p < .05), with additional electrical synchrony induced by HOT-CRT (87.6 ± 6.7 ms, p < .05) and LOT-CRT (73.9 ± 7.6 ms, p < .05). Patients with slow myocardium CV benefit more from CSP compared to CRT (BIVAT-90: CRT 134.5 ± 24.1 ms; HBP 97.1 ± 9.9 ms, p < .01; LBBP: 101.5 ± 10.7 ms, p < .01). Septal but not lateral wall scar made CSP ineffective, while CRT was able to resynchronize the ventricles in the presence of septal scar (BIVAT-90: baseline 119.1 ± 10.8 ms vs. CRT 85.1 ± 14.9 ms, p < .01).

CONCLUSION

Severe LV His-Purkinje conduction disease attenuates the benefits of CSP, with additional improvements achieved with HOT-CRT and LOT-CRT. Septal but not lateral wall scars make CSP ineffective.

Assessing the arrhythmogenic risk of engineered heart tissue patches through in silico application on infarcted ventricle models

BACKGROUND

Post myocardial infarction (MI) ventricles contain fibrotic tissue and may have disrupted electrical properties, both of which predispose to an increased risk of life-threatening arrhythmias. Application of epicardial patches obtained from human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are a potential long-term therapy to treat heart failure resulting from post MI remodelling. However, whether the introduction of these patches is anti- or pro-arrhythmic has not been studied.

METHODS

We studied arrhythmic risk using in silico engineered heart tissue (EHT) patch engraftment on human post-MI ventricular models. Two patient models were studied, including one with a large dense scar and one with an apparent channel of preserved viability bordered on both sides by scar. In each heart model a virtual EHT patch was introduced as a layer of viable tissue overlying the scarred area, with hiPSC-CMs electrophysiological properties. The incidence of re-entrant and sustained activation in simulations with and without EHT patches was assessed and the arrhythmia inducibility compared in the context of different EHT patch properties (conduction velocity (CV) and action potential duration (APD)). The impact of the EHT patch on the likelihood of focal ectopic impulse propagation was estimated by assessing the minimum stimulus strength and duration required to generate a propagating impulse in the scar border zone (BZ) with and without patch.

RESULTS

We uncovered two main mechanisms by which ventricular tachycardia (VT) risk could be either augmented or attenuated by the interaction of the patch with the tissue. In the case of isthmus-related VT, our simulations predict that EHT patches can prevent the induction of VT when the, generally longer, hiPSC-CMs APD is reduced towards more physiological values. In the case of large dense scar, we found that, an EHT patch with CV similar to the host myocardium does not promote VT, while EHT patches with lower CV increase the risk of VT, by promoting both non-sustained and sustained re-entry. Finally, our simulations indicate that electrically coupled EHT patches reduce the likelihood of propagation of focal ectopic impulses.

CONCLUSIONS

The introduction of EHT patches as a treatment for heart failure has the potential to augment or attenuate the risk of ventricular arrhythmias, and variations in the anatomic configuration of the substrate, the functional properties of the BZ and the electrophysiologic properties of the patch itself will determine the overall impact. Planning for delivery of this therapy will need to consider the possible impact on arrhythmia.

Non-invasive localization of post-infarct ventricular tachycardia exit sites to guide ablation planning: a computational deep learning platform utilizing the 12-lead electrocardiogram and intracardiac electrograms from implanted devices

AIMS

Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on localizing sites critical to the maintenance of the clinical arrhythmia, not always recorded on the 12-lead ECG. Targeting the clinical VT by utilizing electrograms (EGM) recordings stored in implanted devices may aid ablation planning, enhancing safety and speed and potentially reducing the need of VT induction. In this context, we aim to develop a non-invasive computational-deep learning (DL) platform to localize VT exit sites from surface ECGs and implanted device intracardiac EGMs.

METHODS AND RESULTS

A library of ECGs and EGMs from simulated paced beats and representative post-infarct VTs was generated across five torso models. Traces were used to train DL algorithms to localize VT sites of earliest systolic activation; first tested on simulated data and then on a clinically induced VT to show applicability of our platform in clinical settings. Localization performance was estimated via localization errors (LEs) against known VT exit sites from simulations or clinical ablation targets. Surface ECGs successfully localized post-infarct VTs from simulated data with mean LE = 9.61 ± 2.61 mm across torsos. VT localization was successfully achieved from implanted device intracardiac EGMs with mean LE = 13.10 ± 2.36 mm. Finally, the clinically induced VT localization was in agreement with the clinical ablation volume.

CONCLUSION

The proposed framework may be utilized for direct localization of post-infarct VTs from surface ECGs and/or implanted device EGMs, or in conjunction with efficient, patient-specific modelling, enhancing safety and speed of ablation planning.

Treatment Planning for Atrial Fibrillation Using Patient-Specific Models Showing the Importance of Fibrillatory-Areas

Computational models have made it possible to study the effect of fibrosis and scar on atrial fibrillation (AF) and plan future personalized treatments. Here, we study the effect of area available for fibrillatory waves to sustain AF. Then we use it to plan for AF ablation to improve procedural outcomes. CARPentry was used to create patient-specific models to determine the association between the size of residual contiguous areas available for AF wavefronts to propagate and sustain AF [fibrillatory area (FA)] after ablation with procedural outcomes. The FA was quantified in a novel manner accounting for gaps in ablation lines. We selected 30 persistent AF patients with known ablation outcomes. We divided the atrial surface into five areas based on ablation scar pattern and anatomical landmarks and calculated the FAs. We validated the models based on clinical outcomes and suggested future ablation lines that minimize the FAs and terminate rotor activities in simulations. We also simulated the effects of three common antiarrhythmic drugs. In the patient-specific models, the predicted arrhythmias matched the clinical outcomes in 25 of 30 patients (accuracy 83.33%). The average largest FA (FAmax) in the recurrence group was 8517 ± 1444 vs. 6772 ± 1531 mm2 in the no recurrence group (p < 0.004). The final FAs after adding the suggested ablation lines in the AF recurrence group reduced the average FAmax from 8517 ± 1444 to 6168 ± 1358 mm2 (p < 0.001) and stopped the sustained rotor activity. Simulations also correctly anticipated the effect of antiarrhythmic drugs in 5 out of 6 patients who used drug therapy post unsuccessful ablation (accuracy 83.33%). Sizes of FAs available for AF wavefronts to propagate are important determinants for ablation outcomes. FA size in combination with computational simulations can be used to direct ablation in persistent AF to minimize the critical mass required to sustain recurrent AF.

Leadless biventricular left bundle and endocardial lateral wall pacing versus left bundle only pacing in left bundle branch block patients

Biventricular endocardial (BIV-endo) pacing and left bundle pacing (LBP) are novel delivery methods for cardiac resynchronization therapy (CRT). Both pacing methods can be delivered through leadless pacing, to avoid risks associated with endocardial or transvenous leads. We used computational modelling to quantify synchrony induced by BIV-endo pacing and LBP through a leadless pacing system, and to investigate how the right-left ventricle (RV-LV) delay, RV lead location and type of left bundle capture affect response. We simulated ventricular activation on twenty-four four-chamber heart meshes inclusive of His-Purkinje networks with left bundle branch block (LBBB). Leadless biventricular (BIV) pacing was simulated by adding an RV apical stimulus and an LV lateral wall stimulus (BIV-endo lateral) or targeting the left bundle (BIV-LBP), with an RV-LV delay set to 5 ms. To test effect of prolonged RV-LV delays and RV pacing location, the RV-LV delay was increased to 35 ms and/or the RV stimulus was moved to the RV septum. BIV-endo lateral pacing was less sensitive to increased RV-LV delays, while RV septal pacing worsened response compared to RV apical pacing, especially for long RV-LV delays. To investigate how left bundle capture affects response, we computed 90% BIV activation times (BIVAT-90) during BIV-LBP with selective and non-selective capture, and left bundle branch area pacing (LBBAP), simulated by pacing 1 cm below the left bundle. Non-selective LBP was comparable to selective LBP. LBBAP was worse than selective LBP (BIVAT-90: 54.2 ± 5.7 ms vs. 62.7 ± 6.5, p < 0.01), but it still significantly reduced activation times from baseline. Finally, we compared leadless LBP with RV pacing against optimal LBP delivery through a standard lead system by simulating BIV-LBP and selective LBP alone with and without optimized atrioventricular delay (AVD). Although LBP alone with optimized AVD was better than BIV-LBP, when AVD optimization was not possible BIV-LBP outperformed LBP alone, because the RV pacing stimulus shortened RV activation (BIVAT-90: 54.2 ± 5.7 ms vs. 66.9 ± 5.1 ms, p < 0.01). BIV-endo lateral pacing or LBP delivered through a leadless system could potentially become an alternative to standard CRT. RV-LV delay, RV lead location and type of left bundle capture affect leadless pacing efficacy and should be considered in future trial designs.

Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models

Computational Fluid Dynamics (CFD) is used to assist in designing artificial valves and planning procedures, focusing on local flow features. However, assessing the impact on overall cardiovascular function or predicting longer-term outcomes may requires more comprehensive whole heart CFD models. Fitting such models to patient data requires numerous computationally expensive simulations, and depends on specific clinical measurements to constrain model parameters, hampering clinical adoption. Surrogate models can help to accelerate the fitting process while accounting for the added uncertainty. We create a validated patient-specific four-chamber heart CFD model based on the Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators (GPEs) as a surrogate model for performing a variance-based global sensitivity analysis (GSA). GSA identified preload as the dominant driver of flow in both the right and left side of the heart, respectively. Left-right differences were seen in terms of vascular outflow resistances, with pulmonary artery resistance having a much larger impact on flow than aortic resistance. Our results suggest that GPEs can be used to identify parameters in personalized whole heart CFD models, and highlight the importance of accurate preload measurements.

Predicting arrhythmia recurrence following catheter ablation for ventricular tachycardia using late gadolinium enhancement magnetic resonance imaging: Implications of varying scar ranges

BACKGROUND

Thresholding-based analysis of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) can create scar maps and identify corridors that might provide a reentrant substrate for ventricular tachycardia (VT). Current recommendations use a full-width-at-half-maximum approach, effectively classifying areas with a pixel signal intensity (PSI) >40% as border zone (BZ) and >60% as core.

OBJECTIVE

The purpose of this study was to investigate the impact of 4 different threshold settings on scar and corridor quantification and to correlate this with postablation VT recurrence.

METHODS

Twenty-seven patients with ischemic cardiomyopathy who had undergone catheter ablation for VT were included for retrospective analysis. LGE-CMR images were analyzed using ADAS3D LV. Scar maps were created for 4 PSI thresholds (40-60, 35-65, 30-70, and 45-55), and the extent of variation in BZ and core, as well as the number and weight of conduction corridors, were quantified. Three-dimensional representations were reconstructed from exported segmentations and used to quantify the surface area between healthy myocardium and scar (BZ + core), and between BZ and core.

RESULTS

A wider PSI threshold was associated with an increase in BZ mass and decrease in scar (P <.001). No significant differences were observed for the total number of corridors and their mass with increasing PSI threshold. The best correlation in predicting arrhythmia recurrence was observed for PSI 45-55 (area under the curve 0.807; P = .001).

CONCLUSION

Varying PSI has a significant impact on quantification of LGE-CMR parameters and may have incremental clinical value in predicting arrhythmia recurrence. Further prospective investigation is warranted to clarify the functional implications of these findings for LGE-CMR-guided ventricular ablation.

A personalized real-time virtual model of whole heart electrophysiology

Computer models capable of representing the intrinsic personal electrophysiology (EP) of the heart in silico are termed virtual heart technologies. When anatomy and EP are tailored to individual patients within the model, such technologies are promising clinical and industrial tools. Regardless of their vast potential, few virtual technologies simulating the entire organ-scale EP of all four-chambers of the heart have been reported and widespread clinical use is limited due to high computational costs and difficulty in validation. We thus report on the development of a novel virtual technology representing the electrophysiology of all four-chambers of the heart aiming to overcome these limitations. In our previous work, a model of ventricular EP embedded in a torso was constructed from clinical magnetic resonance image (MRI) data and personalized according to the measured 12 lead electrocardiogram (ECG) of a single subject under normal sinus rhythm. This model is then expanded upon to include whole heart EP and a detailed representation of the His-Purkinje system (HPS). To test the capacities of the personalized virtual heart technology to replicate standard clinical morphological ECG features under such conditions, bundle branch blocks within both the right and the left ventricles under two different conduction velocity settings are modeled alongside sinus rhythm. To ensure clinical viability, model generation was completely automated and simulations were performed using an efficient real-time cardiac EP simulator. Close correspondence between the measured and simulated 12 lead ECG was observed under normal sinus conditions and all simulated bundle branch blocks manifested relevant clinical morphological features.

Comparison between conduction system pacing and cardiac resynchronization therapy in right bundle branch block patients

A significant number of right bundle branch block (RBBB) patients receive cardiac resynchronization therapy (CRT), despite lack of evidence for benefit in this patient group. His bundle (HBP) and left bundle pacing (LBP) are novel CRT delivery methods, but their effect on RBBB remains understudied. We aim to compare pacing-induced electrical synchrony during conventional CRT, HBP, and LBP in RBBB patients with different conduction disturbances, and to investigate whether alternative ways of delivering LBP improve response to pacing. We simulated ventricular activation on twenty-four four-chamber heart geometries each including a His-Purkinje system with proximal right bundle branch block (RBBB). We simulated RBBB combined with left anterior and posterior fascicular blocks (LAFB and LPFB). Additionally, RBBB was simulated in the presence of slow conduction velocity (CV) in the myocardium, left ventricular (LV) or right ventricular (RV) His-Purkinje system, and whole His-Purkinje system. Electrical synchrony was measured by the shortest interval to activate 90% of the ventricles (BIVAT-90). Compared to baseline, HBP significantly improved activation times for RBBB alone (BIVAT-90: 66.9 ± 5.5 ms vs. 42.6 ± 3.8 ms, p < 0.01), with LAFB (69.5 ± 5.0 ms vs. 58.1 ± 6.2 ms, p < 0.01), with LPFB (81.8 ± 6.6 ms vs. 62.9 ± 6.2 ms, p < 0.01), with slow myocardial CV (119.4 ± 11.4 ms vs. 97.2 ± 10.0 ms, p < 0.01) or slow CV in the whole His-Purkinje system (102.3 ± 7.0 ms vs. 75.5 ± 5.2 ms, p < 0.01). LBP was only effective in RBBB cases if combined with anodal capture of the RV septum myocardium (BIVAT-90: 66.9 ± 5.5 ms vs. 48.2 ± 5.2 ms, p < 0.01). CRT significantly reduced activation times in RBBB in the presence of severely slow RV His-Purkinje CV (95.1 ± 7.9 ms vs. 84.3 ± 9.3 ms, p < 0.01) and LPFB (81.8 ± 6.6 ms vs. CRT: 72.9 ± 8.6 ms, p < 0.01). Both CRT and HBP were ineffective with severely slow CV in the LV His-Purkinje system. HBP is effective in RBBB patients with otherwise healthy myocardium and Purkinje system, while CRT and LBP are ineffective. Response to LBP improves when LBP is combined with RV septum anodal capture. CRT is better than HBP only in patients with severely slow CV in the RV His-Purkinje system, while CV slowing of the whole His-Purkinje system and the myocardium favor HBP over CRT.

Comparison of Propagation Models and Forward Calculation Methods on Cellular, Tissue and Organ Scale Atrial Electrophysiology

The bidomain model and the finite element method are an established standard to mathematically describe cardiac electrophysiology, but are both suboptimal choices for fast and large-scale simulations due to high computational costs. We investigate to what extent simplified approaches for propagation models (monodomain, reaction-Eikonal and Eikonal) and forward calculation (boundary element and infinite volume conductor) deliver markedly accelerated, yet physiologically accurate simulation results in atrial electrophysiology.

METHODS

We compared action potential durations, local activation times (LATs), and electrocardiograms (ECGs) for sinus rhythm simulations on healthy and fibrotically infiltrated atrial models.

RESULTS

All simplified model solutions yielded LATs and P waves in accurate accordance with the bidomain results. Only for the Eikonal model with pre-computed action potential templates shifted in time to derive transmembrane voltages, repolarization behavior notably deviated from the bidomain results. ECGs calculated with the boundary element method were characterized by correlation coefficients 0.9 compared to the finite element method. The infinite volume conductor method led to lower correlation coefficients caused predominantly by systematic overestimations of P wave amplitudes in the precordial leads.

CONCLUSION

Our results demonstrate that the Eikonal model yields accurate LATs and combined with the boundary element method precise ECGs compared to markedly more expensive full bidomain simulations. However, for an accurate representation of atrial repolarization dynamics, diffusion terms must be accounted for in simplified models.

SIGNIFICANCE

Simulations of atrial LATs and ECGs can be notably accelerated to clinically feasible time frames at high accuracy by resorting to the Eikonal and boundary element methods.

An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias

Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.

Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration

Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, an unloaded reference configuration is generated, where a novel line search strategy to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.

A coupling strategy for a first 3D-1D model of the cardiovascular system to study the effects of pulse wave propagation on cardiac function

A key factor governing the mechanical performance of the heart is the bidirectional coupling with the vascular system, where alterations in vascular properties modulate the pulsatile load imposed on the heart. Current models of cardiac electromechanics (EM) use simplified 0D representations of the vascular system when coupling to anatomically accurate 3D EM models is considered. However, these ignore important effects related to pulse wave transmission. Accounting for these effects requires 1D models, but a 3D-1D coupling remains challenging. In this work, we propose a novel, stable strategy to couple a 3D cardiac EM model to a 1D model of blood flow in the largest systemic arteries. For the first time, a personalised coupled 3D-1D model of left ventricle and arterial system is built and used in numerical benchmarks to demonstrate robustness and accuracy of our scheme over a range of time steps. Validation of the coupled model is performed by investigating the coupled system's physiological response to variations in the arterial system affecting pulse wave propagation, comprising aortic stiffening, aortic stenosis or bifurcations causing wave reflections. Our first 3D-1D coupled model is shown to be efficient and robust, with negligible additional computational costs compared to 3D-0D models. We further demonstrate that the calibrated 3D-1D model produces simulated data that match with clinical data under baseline conditions, and that known physiological responses to alterations in vascular resistance and stiffness are correctly replicated. Thus, using our coupled 3D-1D model will be beneficial in modelling studies investigating wave propagation phenomena.

Non-invasive delineation of ventricular tachycardia substrates for cardiac stereotactic body radiotherapy: utility of in-silico pace-mapping

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): EPSRC

Background

Cardiac stereotactive body radiotherapy (CSBRT) is an emerging, non-invasive ablation modality that targets ventricular tachycardia (VT) substrates in patients with limited conventional treatment options. Success of CSBRT hinges primarily on the correct identification of VT targets, which requires non-invasive planning. Current non-invasive, pre-procedure strategies employ multi-electrode electrocardiographic imaging (ECGi). Given its significant cost and potential challenges in detecting endocardial, intramural and/or septal VT sites, there is a need to optimise VT delineation strategies for CSBRT; patient-specific simulations may show promise at guiding such planning non-invasively.

Purpose

We aim to perform non-invasive, in-silico pace-mapping on an image-based computational model to identify VT substrates for CSBRT. We intend to show the utility of our fast computational pipeline - relying on CT imaging data only - to provide further insights on inaccessible, scar-related VT episodes.

Methods

A detailed computational torso model of a CSBRT candidate with incessant VT was generated from CT imaging data. Extracellular content volumes (ECVs) were used to identify different tissue types (healthy, border zone and non-conducting), and scale model tissue conductivities accordingly. In-silico pace-mapping was performed by simulating ~360 paced beats across the LV, and computing corresponding 12-lead ECGs within a fast electrophysiological (EP) simulation environment combining reaction-eikonal and lead field methods. QRS complexes from simulated paced beats were used to construct the virtual correlation pace-map against the measured QRS of the clinically-induced VT, along with a ‘reference-less’ virtual pace-map constructed from neighbouring paced-beat QRSs (within a 20 mm radius). An epicardial activation map of the clinically-induced VT was reconstructed from ECGi measurement, and used for comparison against our virtual pace-maps.

Results

Correlations between simulated paced-beat QRS complexes and the clinically-induced VT QRS were higher in mid-apical, infero-septal segments - segment 9 (85.71%), 10 (87.95%) and 15 (89.58%) - identifying septal origin and pathway of the induced re-entrant circuit. A possible septal VT isthmus was also identified by a high gradient in the virtual reference-less pace-map in segment 9 (&gt; 2.5%/mm). Our in-silico predictions were in agreement with the clinical regions identified for CSBRT (segment 9 and 15), and provided additional information on the 3D and septal dynamics of the VT episode.

Conclusions

Our in-silico pace-mapping study successfully localised VT substrates in a patient unable to receive standard ablative procedures, and provided further clinical insight on the induced VT dynamics. Our rapid in-silico pace-mapping approach may be utilised to support optimal identification of VT target volumes for CSBRT.

Subthreshold delayed afterdepolarizations mediated by reduced tissue conductivity form a substrate for unidirectional block and reentry within the infarcted heart

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): British Heart Foundation, Wellcome Trust

Background

Delayed afterdepolarizations (DADs) due to spontaneous calcium release (SCR) events at the subcellular scale have been associated with arrhythmia formation in the border zone (BZ) of infarcted hearts. DADs may not only summate to form ectopic focal sources but may also inactivate sodium channels forming a substrate for unidirectional conduction block and reentry. The role played by infarct anatomy and altered intracellular coupling in facilitating this phenomenon is not fully understood.

Purpose

To use computational modelling to investigate the role of anatomical properties of the infarct BZ in creating a substrate for DAD-mediated conduction block and reentry.

Methods

MRI data from a porcine post-infarction heart was used to build the computational model. A phenomenological model was used to simulate SCRs in the BZ. Arrhythmia susceptibility was quantified by pacing the model followed by a pause, to see whether DADs would occur, and an extra S2 beat with different coupling intervals (CIs). Tissue conductivity in the BZ was decreased to investigate the effect of uncoupling on DAD-mediated conduction block.

Results

Subthreshold DADs occurring within the infarct BZ inactivated the fast sodium channels which resulted in block of S2 beats. This occurred most readily in narrow isthmuses where electrotonic load was attenuated by the non-conducting scar. DADs rendered the entire isthmus area refractory establishing a substrate for unidirectional block and reentry (see Fig. A). Reduced tissue conductivity in the BZ reduced electrotonic load on cells undergoing DADs. This led to more local tissue depolarization (Vm) as uncoupling prevented current from flowing to neighboring cells at rest (Fig. B-C). Reduced tissue conductivity also enhanced DAD-mediated block by increasing the vulnerable window for reentry initiation (700ms &lt; S2 CI &lt; 900ms as shown in Fig. D).

Conclusion

Subthreshold DADs provide a substrate for arrhythmogenesis in the infarct BZ. Tissue uncoupling enhanced the arrhythmogenic risk by increasing the time window of unidirectional block.

Automated detection of scar-related ventricular tachycardia origins from implanted device electrograms: a combined computational-AI platform

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): EPSRC

Background

Existing strategies that identify ventricular tachycardia (VT) ablation targets either employ invasive and time-consuming electrophysiological (EP) mapping, or non-invasive modalities that utilise standard electrocardiogram (ECG) signals. Success of these pre-procedure ablation approaches in localising re-entrant VTs often relies on VT induction, which could be avoided by utilising recordings of clinical VT episodes stored as electrograms (EGMs) in implanted devices. Such a non-invasive approach that localises VT substrates from EGMs may aid ablation planning, enhancing safety and speed.

Purpose

Our goal is to automate scar-related VT localisation by utilising EGM recordings of VT episodes from implanted devices. To achieve this, deep-learning algorithms will be trained on computational data to return VT sites of origin from implanted device EGMs. Ultimately, we intend to utilise this computational-artificial intelligence (AI) framework to detect ablation targets of clinical VT episodes and guide pre-procedure ablation planning non-invasively.

Methods

A comprehensive library of ECGs and EGMs from simulated paced beats (~15000) and scar-related VTs (500) was generated across five detailed torso models within a fast EP computational environment, combining reaction-eikonal and lead field methods. ECG (or EGM) traces from simulated paced beats were used to initially pre-train two convolutional neural network (CNN) long short-term (LSTM) attention-based architectures. Subsequently, signals of the in-silico, re-entrant VTs were used to re-train the networks to output the sites of origin of these episodes in a standardised ventricular coordinate space. Finally, the retrained CNN architectures were tested on re-entrant VTs of unseen models, and median localisation errors (LEs) were estimated against known VT origins from simulations.

Results

The performance of the networks to localise scar-related VT episodes was asserted for each torso model. When a torso model was only seen during initial training on simulated paced beats, implanted device EGMs and ECGs successfully localised VT sources with LEs 10.04 – 16.36 mm and 10.05 – 12.79 mm, respectively. When a torso model was not seen during pacing or VT training, recreating potential clinical application settings where ECGs or EGMs of clinical VTs would be the only inputs to the networks, LEs ranged 12.42 - 22.79 mm and 12.41 - 19.68 mm for EGM and ECG-based testing, respectively.

Conclusions

Our study successfully detected VT ablation substrates with accuracy that could be beneficial in clinical ablation settings. The proposed computational-AI framework may be used to automate the localisation of scar-related VTs from clinical ECGs or EGM recordings from implanted devices, ultimately aiding ablation planning.

Correction: Linking statistical shape models and simulated function in the healthy adult human heart

[This corrects the article DOI: 10.1371/journal.pcbi.1008851.].

An accurate, robust, and efficient finite element framework with applications to anisotropic, nearly and fully incompressible elasticity

Fiber-reinforced soft biological tissues are typically modeled as hyperelastic, anisotropic, and nearly incompressible materials. To enforce incompressibility a multiplicative split of the deformation gradient into a volumetric and an isochoric part is a very common approach. However, the finite element analysis of such problems often suffers from severe volumetric locking effects and numerical instabilities. In this paper, we present novel methods to overcome volumetric locking phenomena for using stabilized P1-P1 elements. We introduce different stabilization techniques and demonstrate the high robustness and computational efficiency of the chosen methods. In two benchmark problems from the literature as well as an advanced application to cardiac electromechanics, we compare the approach to standard linear elements and show the accuracy and versatility of the methods to simulate anisotropic, nearly and fully incompressible materials. We demonstrate the potential of this numerical framework to accelerate accurate simulations of biological tissues to the extent of enabling patient-specific parameterization studies, where numerous forward simulations are required.

Modelling the interaction between stem cells derived cardiomyocytes patches and host myocardium to aid non-arrhythmic engineered heart tissue design

Application of epicardial patches constructed from human-induced pluripotent stem cell- derived cardiomyocytes (hiPSC-CMs) has been proposed as a long-term therapy to treat scarred hearts post myocardial infarction (MI). Understanding electrical interaction between engineered heart tissue patches (EHT) and host myocardium represents a key step toward a successful patch engraftment. EHT retain different electrical properties with respect to the host heart tissue due to the hiPSC-CMs immature phenotype, which may lead to increased arrhythmia risk. We developed a modelling framework to examine the influence of patch design on electrical activation at the engraftment site. We performed an in silico investigation of different patch design approaches to restore pre-MI activation properties and evaluated the associated arrhythmic risk. We developed an in silico cardiac electrophysiology model of a transmural cross section of host myocardium. The model featured an infarct region, an epicardial patch spanning the infarct region and a bath region. The patch is modelled as a layer of hiPSC-CM, combined with a layer of conductive polymer (CP). Tissue and patch geometrical dimensions and conductivities were incorporated through 10 modifiable model parameters. We validated our model against 4 independent experimental studies and showed that it can qualitatively reproduce their findings. We performed a global sensitivity analysis (GSA) to isolate the most important parameters, showing that the stimulus propagation is mainly governed by the scar depth, radius and conductivity when the scar is not transmural, and by the EHT patch conductivity when the scar is transmural. We assessed the relevance of small animal studies to humans by comparing simulations of rat, rabbit and human myocardium. We found that stimulus propagation paths and GSA sensitivity indices are consistent across species. We explored which EHT design variables have the potential to restore physiological propagation. Simulations predict that increasing EHT conductivity from 0.28 to 1-1.1 S/m recovered physiological activation in rat, rabbit and human. Finally, we assessed arrhythmia risk related to increasing EHT conductivity and tested increasing the EHT Na+ channel density as an alternative strategy to match healthy activation. Our results revealed a greater arrhythmia risk linked to increased EHT conductivity compared to increased Na+ channel density. We demonstrated that our modeling framework could capture the interaction between host and EHT patches observed in in vitro experiments. We showed that large (patch and tissue dimensions) and small (cardiac myocyte electrophysiology) scale differences between small animals and humans do not alter EHT patch effect on infarcted tissue. Our model revealed that only when the scar is transmural do EHT properties impact activation times and isolated the EHT conductivity as the main parameter influencing propagation. We predicted that restoring physiological activation by tuning EHT conductivity is possible but may promote arrhythmic behavior. Finally, our model suggests that acting on hiPSC-CMs low action potential upstroke velocity and lack of IK1 may restore pre-MI activation while not promoting arrhythmia.

Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function

Cardiac fiber direction is an important factor determining the propagation of electrical activity, as well as the development of mechanical force. In this article, we imaged the ventricles of several species with special attention to the intraventricular septum to determine the functional consequences of septal fiber organization. First, we identified a dual-layer organization of the fiber orientation in the intraventricular septum of ex vivo sheep hearts using diffusion tensor imaging at high field MRI. To expand the scope of the results, we investigated the presence of a similar fiber organization in five mammalian species (rat, canine, pig, sheep, and human) and highlighted the continuity of the layer with the moderator band in large mammalian species. We implemented the measured septal fiber fields in three-dimensional electromechanical computer models to assess the impact of the fiber orientation. The downward fibers produced a diamond activation pattern superficially in the right ventricle. Electromechanically, there was very little change in pressure volume loops although the stress distribution was altered. In conclusion, we clarified that the right ventricular septum has a downwardly directed superficial layer in larger mammalian species, which can have modest effects on stress distribution.

NEW & NOTEWORTHY A dual-layer organization of the fiber orientation in the intraventricular septum was identified in ex vivo hearts of large mammals. The RV septum has a downwardly directed superficial layer that is continuous with the moderator band. Electrically, it produced a diamond activation pattern. Electromechanically, little change in pressure volume loops were noticed but stress distribution was altered. Fiber distribution derived from diffusion tensor imaging should be considered for an accurate strain and stress analysis.

An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics

Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications.

Diversity and complexity of the cavotricuspid isthmus in rabbits: A novel scheme for classification and geometrical transformation of anatomical structures

The aim of this study was to describe the morphology of the cavotricuspid isthmus (CTI) in detail and introduce a comprehensive scheme to describe the topology of this region based on functional considerations. This may lead to a better understanding of isthmus-dependent flutter and fibrillation and to improved intervention strategies. We used images of the cavotricuspid isthmus from 52 rabbits of both sexes with a median weight of 3.40 ± 0.93 kg. The area of the CTI was 124.25 ± 42.14 mm² with 53.28 ± 21.13 mm² covered by pectinate muscles connecting the terminal crest and the vestibule. Isthmus length decreased from inferolateral (13.09 ±2.14 mm) to central (9.85 ± 2.14 mm) to paraseptal (4.88 ± 1.96 mm) resembling the overall human geometry. Ramification sites of pectinate muscles were identified and six levels dividing the CTI from posterior to anterior were introduced. This allowed the classification of pectinate muscle segments based on the connected ramification level. To account for the high inter-individual variations in size and shape, the CTI was projected onto a normalized reference frame using bilinear transformation. Furthermore, two measures of complexity were introduced: (i) the ramification index, which reflects the total number of muscle segments connected to a ramification site and (ii) the complexity index, which reflects the type of ramification (branching or merging site). Topological analysis showed that the complexity of the pectinate muscle network decreases from inferolateral to paraseptal and that the number of electrically uncoupled parallel pathways increases in the central section between the terminal crest and the vestibule which introduces potential reentry pathways.

Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction

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 to 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.

Impact of anatomical reverse remodelling in the design of optimal quadripolar pacing leads: A computational study

Lead position is an important factor in determining response to Cardiac Resynchronization Therapy (CRT) in dyssynchronous heart failure (HF) patients. Multipoint pacing (MPP) enables pacing from multiple electrodes within the same lead, improving the potential outcome for patients. Virtual quadripolar lead designs were evaluated by simulating pacing from all combinations of 1 and 2 electrodes along the lead in each virtual patient from cohorts of HF (n = 24) and simulated reverse remodelled (RR, n = 20) patients. Electrical synchrony was assessed by the time 90% of the ventricular myocardium is activated (AT090). Optimal 1 and 2 electrode pacing configurations for AT090 were combined to identify the 4-electrode lead design that maximised benefits across all patients. LV pacing in the HF cohort in all possible single and double electrode locations reduced AT090 by 14.48 ± 5.01 ms (11.92 ± 3.51%). The major determinant of reduction in activation time was patient anatomy. Pacing with a single optimal lead design reduced AT090 more in the HF cohort than the RR cohort (12.68 ± 3.29% vs 10.81 ± 2.34%). Pacing with a single combined HF and RR population-optimised lead design achieves electrical resynchronization with near equivalence to personalised lead designs both in HF and RR anatomies. These findings suggest that although lead configurations have to be tailored to each patient, a single optimal lead design is sufficient to obtain near-optimal results across most patients. This study shows the potential of virtual clinical trials as tools to compare existing and explore new lead designs.

Automated Framework for the Inclusion of a His-Purkinje System in Cardiac Digital Twins of Ventricular Electrophysiology

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.

A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation

Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.

A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation

Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.

Estimation and Validation of Cardiac Conduction Velocity and Wavefront Reconstruction Using Epicardial and Volumetric Data

OBJECTIVE

In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV).

METHODS

We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart.

RESULTS

The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used.

CONCLUSION

Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs.

SIGNIFICANCE

We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.

Non-invasive simulated electrical and measured mechanical indices predict response to cardiac resynchronization therapy

BACKGROUND

Cardiac Resynchronization Therapy (CRT) in dyssynchronous heart failure patients is ineffective in 20-30% of cases. Sub-optimal left ventricular (LV) pacing location can lead to non-response, thus there is interest in LV lead location optimization. Invasive acute haemodynamic response (AHR) measurements have been used to optimize the LV pacing location during CRT implantation. In this manuscript, we aim to predict the optimal lead location (AHR>10%) with non-invasive computed tomography (CT) based measures of cardiac anatomical and mechanical properties, and simulated electrical activation times.

METHODS

Non-invasive measurements from CT images and ECG were acquired from 34 patients indicated for CRT upgrade. The LV lead was implanted and AHR was measured at different pacing sites. Computer models of the ventricles were used to simulate the electrical activation of the heart, track the mechanical motion throughout the cardiac cycle and measure the wall thickness of the LV on a patient specific basis.

RESULTS

We tested the ability of electrical, mechanical and anatomical indices to predict the optimal LV location. Electrical (RV-LV delay) and mechanical (time to peak contraction) indices were correlated with an improved AHR, while wall thickness was not predictive. A logistic regression model combining RV-LV delay and time to peak contraction was able to predict positive response with 70 ± 11% accuracy and AUROC curve of 0.73.

CONCLUSION

Non-invasive electrical and mechanical indices can predict optimal epicardial lead location. Prospective analysis of these indices could allow clinicians to test the AHR at fewer pacing sites and reduce time, costs and risks to patients.

The openCARP simulation environment for cardiac electrophysiology

BACKGROUND AND OBJECTIVE

Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community.

METHODS AND RESULTS

openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential.

CONCLUSION

As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.

The Role of Myocardial Fiber Direction in Epicardial Activation Patterns via Uncertainty Quantification

Fiber structure governs the spread of excitation in the heart; however, little is known about the effects of physiological variability in fiber orientation on epicardial activation. To investigate these effects, we implemented ventricular simulations of activation using rule-based fiber orientations, and robust uncertainty quantification algorithms to capture detailed maps of model sensitivity. Specifically, we implemented polynomial chaos expansion, which allows for robust exploration with reduced computational demand through an emulator function to approximate the underlying forward model. We applied these techniques to examine the activation sequence of the heart in response to both epicardial and endocardial stimuli within the left ventricular free wall and variations in fiber orientation. Our results showed that physiological variation in fiber orientation does not significantly impact the location of activation features, but it does impact the overall spread of activation. Future studies will investigate under which circumstances physiological changes in fiber orientation might alter electrical propagation such that the resulting simulations produce misleading outcomes.

The openCARP simulation environment for cardiac electrophysiology

BACKGROUND AND OBJECTIVE

Cardiac electrophysiology is a medical specialty with a long and rich tradition of computational modeling. Nevertheless, no community standard for cardiac electrophysiology simulation software has evolved yet. Here, we present the openCARP simulation environment as one solution that could foster the needs of large parts of this community.

METHODS AND RESULTS

openCARP and the Python-based carputils framework allow developing and sharing simulation pipelines which automate in silico experiments including all modeling and simulation steps to increase reproducibility and productivity. The continuously expanding openCARP user community is supported by tailored infrastructure. Documentation and training material facilitate access to this complementary research tool for new users. After a brief historic review, this paper summarizes requirements for a high-usability electrophysiology simulator and describes how openCARP fulfills them. We introduce the openCARP modeling workflow in a multi-scale example of atrial fibrillation simulations on single cell, tissue, organ and body level and finally outline future development potential.

CONCLUSION

As an open simulator, openCARP can advance the computational cardiac electrophysiology field by making state-of-the-art simulations accessible. In combination with the carputils framework, it offers a tailored software solution for the scientific community and contributes towards increasing use, transparency, standardization and reproducibility of in silico experiments.