Abstract:

Efficient personalized ablation strategies for treating atrial arrhythmias remain challenging. Discrepancies in identifying arrhythmogenic areas using characterization methods, such as late gadolinium enhanced magnetic resonance imaging (LGE-MRI) and electroanatomical mapping, require a comparative analysis of local impedance (LI) and LGE-MRI data. This study aims to analyze correlations as basis for im- provement of treatment strategies. 16 patients undergoing left atrium (LA) ablation with LGE-MRI acquisition and LI data recording were recruited. LGE-MRI data and LI measurements were normalized to patient- and modality-specific blood pool references. A global mean shape was generated based on all patient geometries, and normalized local impedance (LIN) and LGE-MRI image intensity ratio (IIR) data points were co- registered for comparison.Data analysis comprised intra-patient and inter-patient assess- ments, evaluating differences in LIN values among datasets categorized by their IIR. Due to substantial deviations in LIN values, even within the same patient and IIR-category, discern- ing the presence or absence of a correlation was challenging, and no statistically significant correlation could be identified. Our findings underscore the necessity for standardized proto- cols in data acquisition, processing, and comparison, to mini- mize unquantified confounding effects. While immediate substitution of LI for LGE-MRI seems improbable given the significant LIN variations, this preliminary study lays the ground- work for systematic data acquisition. By ensuring data quality, a meaningful comparison between LI and LGE-MRI data can be facilitated, potentially shaping future strategies for atrial ar- rhythmia treatment.

Abstract:

We investigate the force-frequency relationship (FFR) representation of human ventricular cardiomyocyte models and point out shortcomings, motivated by discrepancies in whole-heart simulations at increased pacing rates. Utilizing the openCARP simulator, simulations across frequencies ranging from 1 Hz to 3 Hz were conducted. Experimental data on healthy human ventricular cardiomyocytes were collected and compared against simulated results. Results show deviations for all models, with Tomek et al. modeling time sensitive biomarkers the best. For example, the ratio of time to peak tension at 2 Hz and 1 Hz is around 85% for experiments, 82% for hybrid data, 95% for Tomek et al., 98% for O’Hara et al. and 138% for ten Tusscher et al. These discrepancies, highlight not only the need for careful selection of ionic models, but also the importance of refining ventricular cardiomyocyte models for advancing in-silico cardiac research.

Abstract:

AIMS: The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability. METHODS AND RESULTS: Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively. CONCLUSION: Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Abstract:

The human heart is subject to highly variable amounts of strain during day-to-day activities and needs to adapt to a wide range of physiological demands. This adaptation is driven by an autoregulatory loop that includes both electrical and the mechanical components. In particular, mechanical forces are known to feed back into the cardiac electrophysiology system, which can result in pro- and anti-arrhythmic effects. Despite the widespread use of computational modelling and simulation for cardiac electrophysiology research, the majority of in silico experiments ignore this mechano-electric feedback entirely due to the high computational cost associated with solving cardiac mechanics. In this study, we therefore use an electromechanically coupled whole-heart model to investigate the differential and combined effects of electromechanical feedback mechanisms with a focus on their physiological relevance during sinus rhythm. In particular, we consider troponin-bound calcium, the effect of deformation on the tissue diffusion tensor, and stretch-activated channels. We found that activation of the myocardium was only significantly affected when including deformation into the diffusion term of the monodomain equation. Repolarization, on the other hand, was influenced by both troponin-bound calcium and stretch-activated channels and resulted in steeper repolarization gradients in the atria. The latter also caused afterdepolarizations in the atria. Due to its central role for tension development, calcium bound to troponin affected stroke volume and pressure. In conclusion, we found that mechano-electric feedback changes activation and repolarization patterns throughout the heart during sinus rhythm and lead to a markedly more heterogeneous electrophysiological substrate. KEY POINTS: The electrophysiological and mechanical function of the heart are tightly interrelated by excitation-contraction coupling (ECC) in the forward direction and mechano-electric feedback (MEF) in the reverse direction. While ECC is considered in many state-of-the-art computational models of cardiac electromechanics, less is known about the effect of different MEF mechanisms. Accounting for calcium bound to troponin increases stroke volume and delays repolarization. Geometry-mediated MEF leads to more heterogeneous activation and repolarization with steeper gradients. Both effects combine in an additive way. Non-selective stretch-activated channels as an additional MEF mechanism lead to heterogeneous diastolic transmembrane voltage, higher developed tension and delayed repolarization or afterdepolarizations in highly stretched parts of the atria. The differential and combined effects of these three MEF mechanisms during sinus rhythm activation in a human four-chamber heart model may have implications for arrhythmogenesis, both in terms of substrate (repolarization gradients) and triggers (ectopy).

Abstract:

Background and Aims Patients with persistent atrial fibrillation (AF) experience 50% recurrence despite pulmonary vein isolation (PVI), and no consensus is established for second treatments. The aim of our i-STRATIFICATION study is to provide evidence for stratifying patients with AF recurrence after PVI to optimal pharmacological and ablation therapies, through in-silico trials.Methods A cohort of 800 virtual patients, with variability in atrial anatomy, electrophysiology, and tissue structure (low voltage areas, LVA), was developed and validated against clinical data from ionic currents to ECG. Virtual patients presenting AF post-PVI underwent 13 secondary treatments.Results Sustained AF developed in 522 virtual patients after PVI. Second ablation procedures involving left atrial ablation alone showed 55% efficacy, only succeeding in small right atria (<60mL). When additional cavo-tricuspid isthmus ablation was considered, Marshall-Plan sufficed (66% efficacy) for small left atria (<90mL). For bigger left atria, a more aggressive ablation approach was required, such as anterior mitral line (75% efficacy) or posterior wall isolation plus mitral isthmus ablation (77% efficacy). Virtual patients with LVA greatly benefited from LVA ablation in the left and right atria (100% efficacy). Conversely, in the absence of LVA, synergistic ablation and pharmacotherapy could terminate AF. In the absence of ablation, the patient’s ionic current substrate modulated the response to antiarrhythmic drugs, being the inward currents critical for optimal stratification to amiodarone or vernakalant.Conclusion In-silico trials identify optimal strategies for AF treatment based on virtual patient characteristics, evidencing the power of human modelling and simulation as a clinical assisting tool.

Abstract:

Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. We found that the SHAP and LIME methods and Chi-squared test all worked well together with the native Random forest and Logistic regression feature rankings. Some methods gave inconsistent results, which included the Maximum Relevance Minimum Redundancy and Neighbourhood Component Analysis methods. The permutation-based methods generally performed quite poorly. A surprising result was found in the case of left bundle branch block, where T-wave morphology features were consistently identified as being important for diagnosis, but are not used by clinicians.

Abstract:

We investigate the properties of static mechanical and dynamic electro-mechanical models for the deformation of the human heart. Numerically this is realized by a staggered scheme for the coupled partial/ordinary differential equation (PDE-ODE) system. First, we consider a static and purely mechanical benchmark configuration on a realistic geometry of the human ventricles. Using a penalty term for quasi-incompressibility, we test different parameters and mesh sizes and observe that this approach is not sufficient for lowest order conforming finite elements. Then, we compare the approaches of active stress and active strain for cardiac muscle contraction. Finally, we compare in a coupled anatomically realistic electro-mechanical model numerical Newmark damping with a visco-elastic model using Rayleigh damping. Nonphysiological oscillations can be better mitigated using viscosity.

Abstract:

IntroductionThe role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients until now. It remains unclear whether the incorporation of the RA influences the propensity of the model to reentry induction. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain.AimTo evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability.MethodsImaging data from 8 subjects were obtained to generate patient-specific computer models. For each subject, we created 2 models: one monoatrial consisting only of the LA, and one biatrial model consisting of both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche et al. cellular model was modified from control conditions to a setup representing AF-induced remodeling with 0%, 50%, and 100% changes for H, M, and S, respectively. Conduction velocity along the myocyte preferential direction was set to 1.2, 1.0, and 0.8m/s for each remodeling level. To incorporate fibrotic substrate, we manually placed six seeds on each biatrial model, 3 in the LA and 3 in the RA, corresponding to regions with the most frequent enhancement (IIR>1.2) in LGE-MRI. The extent of the fibrotic substrate corresponded to the Utah 2 (5-20%) and Utah 4 (>35%) stages, for M and S respectively, while the H state was modeled without fibrosis. Electrical propagation in the atria was modeled using the monodomain equation solved with openCARP. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm. A point was classified as inducing arrhythmia if reentry was initiated and maintained for at least 1s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) of the induced arrhythmia was assessed at the stimulation site. We compared the vulnerability ratio of the LA in monoatrial and biatrial configurations.ResultsThe incorporation of the RA increased the mean LA vulnerability ratio by 115.79% (0.19±0.13 to 0.41±0.22, p=0.033) in state M, and 29.03% in state S (0.31±0.14 to 0.40±0.15, p=0.219) as illustrated in Figure 1. No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.51% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in M scenario, and decreased it by 7.17% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion resulted in an elevated LA inducibility, revealing 4.9±3.3 additional points per patient in the LA for the biatrial model that did not induce reentry in the monoatrial model. ConclusionsThe LA vulnerability in a biatrial model differs from the LA vulnerability in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models.