Effects of ranolazine on the arrhythmic substrate in hypertrophic cardiomyopathy.

Introduction: Hypertrophic cardiomyopathy (HCM) is a leading cause of lethal arrhythmias in the young. Although the arrhythmic substrate has been hypothesised to be amenable to late Na+ block with ranolazine, the specific mechanisms are not fully understood. Therefore, this study aimed to investigate the substrate mechanisms of safety and antiarrhythmic efficacy of ranolazine in HCM. Methods: Computational models of human tissue and ventricles were used to simulate the electrophysiological behaviour of diseased HCM myocardium for variable degrees of repolarisation impairment, validated against in vitro and clinical recordings. S1-S2 pacing protocols were used to quantify arrhythmic risk in scenarios of (i) untreated HCM-remodelled myocardium and (ii) myocardium treated with 3µM, 6µM and 10µM ranolazine, for variable repolarisation heterogeneity sizes and pacing rates. ECGs were derived from biventricular simulations to identify ECG biomarkers linked to antiarrhythmic effects. Results: 10µM ranolazine given to models manifesting ventricular tachycardia (VT) at baseline led to a 40% reduction in number of VT episodes on pooled analysis of >40,000 re-entry inducibility simulations. Antiarrhythmic efficacy and safety were dependent on the degree of repolarisation impairment, with optimal benefit in models with maximum JTc interval <370 ms. Ranolazine increased risk of VT only in models with severe-extreme repolarisation impairment. Conclusion: Ranolazine efficacy and safety may be critically dependent upon the degree of repolarisation impairment in HCM. For moderate repolarisation impairment, reductions in refractoriness heterogeneity by ranolazine may prevent conduction blocks and re-entry. With severe-extreme disease substrates, reductions of the refractory period can increase re-entry sustainability.

Mechanisms of ischaemia-induced arrhythmias in hypertrophic cardiomyopathy: a large-scale computational study.

AIMS: Lethal arrhythmias in hypertrophic cardiomyopathy (HCM) are widely attributed to myocardial ischaemia and fibrosis. How these factors modulate arrhythmic risk remains largely unknown, especially as invasive mapping protocols are not routinely used in these patients. By leveraging multiscale digital-twin technologies, we aim to investigate ischaemic mechanisms of increased arrhythmic risk in HCM. METHODS AND RESULTS: Computational models of human HCM cardiomyocytes, tissue and ventricles were used to simulate outcomes of phase 1A acute myocardial ischaemia. Cellular response predictions were validated with patch-clamp studies of human HCM cardiomyocytes (n=12 cells, N=5 patients). Ventricular simulations were informed by typical distributions of subendocardial/transmural ischaemia as analysed in perfusion scans (N=28 patients). S1-S2 pacing protocols were used to quantify arrhythmic risk for scenarios in which regions of septal obstructive hypertrophy were affected by (i) ischaemia, (ii) ischaemia and impaired repolarisation, and (iii) ischaemia, impaired repolarisation, and diffuse fibrosis.HCM cardiomyocytes exhibited enhanced action potential and abnormal effective refractory period shortening to ischaemic insults. Analysis of c.a. 75,000 re-entry induction cases revealed that the abnormal HCM cellular response enabled establishment of arrhythmia at milder ischaemia than otherwise possible in healthy myocardium, due to larger refractoriness gradients that promoted conduction block. Arrhythmias were more easily sustained in transmural than subendocardial ischaemia. Mechanisms of ischaemia-fibrosis interaction were strongly electrophysiology dependent. Fibrosis enabled asymmetric re-entry patterns and break-up into sustained ventricular tachycardia. CONCLUSIONS: HCM ventricles exhibited an increased risk to non-sustained and sustained re-entry, largely dominated by an impaired cellular response and deleterious interactions with the diffuse fibrotic substrate.

Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials.

Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

Electrophysiological mechanisms underlying T wave pseudonormalisation on stress ECGs in hypertrophic cardiomyopathy.

BACKGROUND: Pseudonormal T waves may be detected on stress electrocardiograms (ECGs) in hypertrophic cardiomyopathy (HCM). Either myocardial ischaemia or purely exercise-induced changes have been hypothesised to contribute to this phenomenon, but the precise electrophysiological mechanisms remain unknown. METHODS: Computational models of human HCM ventricles (n = 20) with apical and asymmetric septal hypertrophy phenotypes with variable severities of repolarisation impairment were used to investigate the effects of acute myocardial ischaemia on ECGs with T wave inversions at baseline. Virtual 12-lead ECGs were derived from a total of 520 biventricular simulations, for cases with regionally ischaemic K+ accumulation in hypertrophied segments, global exercise-induced serum K+ increases, and/or increased pacing frequency, to analyse effects on ECG biomarkers including ST segments, T wave amplitudes, and QT intervals. RESULTS: Regional ischaemic K+ accumulation had a greater impact on T wave pseudonormalisation than exercise-induced serum K+ increases, due to larger reductions in repolarisation gradients. Increases in serum K+ and pacing rate partially corrected T waves in some anatomical and electrophysiological phenotypes. T wave morphology was more sensitive than ST segment elevation to regional K+ increases, suggesting that T wave pseudonormalisation may sometimes be an early, or the only, ECG feature of myocardial ischaemia in HCM. CONCLUSIONS: Ischaemia-induced T wave pseudonormalisation can occur on stress ECG testing in HCM before significant ST segment changes. Some anatomical and electrophysiological phenotypes may enable T wave pseudonormalisation due to exercise-induced increased serum K+ and pacing rate. Consideration of dynamic T wave abnormalities could improve the detection of myocardial ischaemia in HCM.

Ventricular anatomical complexity and sex differences impact predictions from electrophysiological computational models.

The aim of this work was to analyze the influence of sex hormones and anatomical details (trabeculations and false tendons) on the electrophysiology of healthy human hearts. Additionally, sex- and anatomy-dependent effects of ventricular tachycardia (VT) inducibility are presented. To this end, four anatomically normal, human, biventricular geometries (two male, two female), with identifiable trabeculations, were obtained from high-resolution, ex-vivo MRI and represented by detailed and smoothed geometrical models (with and without the trabeculations). Additionally one model was augmented by a scar. The electrophysiology finite element model (FEM) simulations were carried out, using O'Hara-Rudy human myocyte model with sex phenotypes of Yang and Clancy. A systematic comparison between detailed vs smooth anatomies, male vs female normal hearts was carried out. The heart with a myocardial infarction was subjected to a programmed stimulus protocol to identify the effects of sex and anatomical detail on ventricular tachycardia inducibility. All female hearts presented QT-interval prolongation however the prolongation interval in comparison to the male phenotypes was anatomy-dependent and was not correlated to the size of the heart. Detailed geometries showed QRS fractionation and increased T-wave magnitude in comparison to the corresponding smoothed geometries. A variety of sustained VTs were obtained in the detailed and smoothed male geometries at different pacing locations, which provide evidence of the geometry-dependent differences regarding the prediction of the locations of reentry channels. In the female phenotype, sustained VTs were induced in both detailed and smooth geometries with RV apex pacing, however no consistent reentry channels were identified. Anatomical and physiological cardiac features play an important role defining risk in cardiac disease. These are often excluded from cardiac electrophysiology simulations. The assumption that the cardiac endocardium is smooth may produce inaccurate predictions towards the location of reentry channels in in-silico tachycardia inducibility studies.

Mechanism based therapies enable personalised treatment of hypertrophic cardiomyopathy.

Cardiomyopathies have unresolved genotype-phenotype relationships and lack disease-specific treatments. Here we provide a framework to identify genotype-specific pathomechanisms and therapeutic targets to accelerate the development of precision medicine. We use human cardiac electromechanical in-silico modelling and simulation which we validate with experimental hiPSC-CM data and modelling in combination with clinical biomarkers. We select hypertrophic cardiomyopathy as a challenge for this approach and study genetic variations that mutate proteins of the thick (MYH7R403Q/+) and thin filaments (TNNT2R92Q/+, TNNI3R21C/+) of the cardiac sarcomere. Using in-silico techniques we show that the destabilisation of myosin super relaxation observed in hiPSC-CMs drives disease in virtual cells and ventricles carrying the MYH7R403Q/+ variant, and that secondary effects on thin filament activation are necessary to precipitate slowed relaxation of the cell and diastolic insufficiency in the chamber. In-silico modelling shows that Mavacamten corrects the MYH7R403Q/+ phenotype in agreement with hiPSC-CM experiments. Our in-silico model predicts that the thin filament variants TNNT2R92Q/+ and TNNI3R21C/+ display altered calcium regulation as central pathomechanism, for which Mavacamten provides incomplete salvage, which we have corroborated in TNNT2R92Q/+ and TNNI3R21C/+ hiPSC-CMs. We define the ideal characteristics of a novel thin filament-targeting compound and show its efficacy in-silico. We demonstrate that hybrid human-based hiPSC-CM and in-silico studies accelerate pathomechanism discovery and classification testing, improving clinical interpretation of genetic variants, and directing rational therapeutic targeting and design.

Remodelling of potassium currents underlies arrhythmic action potential prolongation under beta-adrenergic stimulation in hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) patients often present an enhanced arrhythmogenicity that can lead to lethal arrhythmias, especially during exercise. Recent studies have indicated an abnormal response of HCM cardiomyocytes to ß-adrenergic receptor stimulation (ß-ARS), with prolongation of their action potential rather than shortening. The mechanisms underlying this aberrant response to sympathetic stimulation and its possible proarrhythmic role remain unknown. The aims of this study are to investigate the key ionic mechanisms underlying the HCM abnormal response to ß-ARS and the resultant repolarisation abnormalities using human-based experimental and computational methodologies. We integrated and calibrated the latest models of human ventricular electrophysiology and ß-ARS using experimental measurements of human adult cardiomyocytes from control and HCM patients. Our major findings include: (1) the developed in silico models of ß-ARS capture the behaviour observed in the experimental data, including the aberrant response of HCM cardiomyocytes to ß-ARS; (2) the reduced increase of potassium currents under ß-ARS was identified as the main mechanism of action potential prolongation in HCM, rather than a more sustained inward calcium current; (3) action potential duration differences between healthy and HCM cardiomyocytes were increased upon ß-ARS, while endocardial to epicardial differences in HCM cardiomyocytes were reduced; (4) models presenting repolarisation abnormalities were characterised by downregulation of the rapid delayed rectifier potassium current and the sodium­potassium pump, while inward currents were upregulated. In conclusion, our results identify causal relationships between the HCM phenotype and its arrhythmogenic response to ß-ARS through the downregulation of potassium currents.

Paradoxical prolongation of QT interval during exercise in patients with hypertrophic cardiomyopathy: cellular mechanisms and implications for diastolic function.

Aims: Ventricular cardiomyocytes from hypertrophic cardiomyopathy (HCM) patient hearts show prolonged action potential duration (APD), impaired intracellular Ca2+ homeostasis and abnormal electrical response to beta -adrenergic stimulation. We sought to determine whether this behaviour is associated with abnormal changes of repolarization during exercise and worsening of diastolic function, ultimately explaining the intolerance to exercise experienced by some patients without obstruction. Methods and results: Non-obstructive HCM patients (178) and control subjects (81) underwent standard exercise testing, including exercise echocardiography. Ventricular myocytes were isolated from myocardial samples of 23 HCM and eight non-failing non-hypertrophic surgical patients. The APD shortening in response to high frequencies was maintained in HCM myocytes, while ß-adrenergic stimulation unexpectedly prolonged APDs, ultimately leading to a lesser shortening of APDs in response to exercise. In HCM vs. control subjects, we observed a lesser shortening of QT interval at peak exercise (QTc: +27 ± 52 ms in HCM, -4 ± 50 ms in controls, P < 0.0001). In patients showing a marked QTc prolongation (>30 ms), the excessive shortening of the electrical diastolic period was linked with a limited increase of heart-rate and deterioration of diastolic function at peak effort. Conclusions: Abnormal balance of Ca2+- and K+-currents in HCM cardiomyocytes determines insufficient APD and Ca2+-transient shortening with exercise. In HCM patients, exercise-induced QTc prolongation was associated with impaired diastolic reserve, contributing to the reduced exercise tolerance. Our results support the idea that severe electrical cardiomyocyte abnormalities underlie exercise intolerance in a subgroup of HCM patients without obstruction.

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.

Pharmacological Management of Hypertrophic Cardiomyopathy: From Bench to Bedside.

Hypertrophic cardiomyopathy (HCM), the most common inherited heart disease, is still orphan of a specific drug treatment. The erroneous consideration of HCM as a rare disease has hampered the design and conduct of large, randomized trials in the last 50 years, and most of the indications in the current guidelines are derived from small non-randomized studies, case series, or simply from the consensus of experts. Guideline-directed therapy of HCM includes non-selective drugs such as disopyramide, non-dihydropyridine calcium channel blockers, or ß-adrenergic receptor blockers, mainly used in patients with symptomatic obstruction of the outflow tract. Following promising preclinical studies, several drugs acting on potential HCM-specific targets were tested in patients. Despite the huge efforts, none of these studies was able to change clinical practice for HCM patients, because tested drugs were proven to be scarcely effective or hardly tolerated in patients. However, novel compounds have been developed in recent years specifically for HCM, addressing myocardial hypercontractility and altered energetics in a direct manner, through allosteric inhibition of myosin. In this paper, we will critically review the use of different classes of drugs in HCM patients, starting from "old" established agents up to novel selective drugs that have been recently trialed in patients.

In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations.

AIMS: A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. METHODS AND RESULTS: An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO. Subsequently, the simulated ECGs were compared with patient ECG data obtained during the clinical tachycardia using the 12-lead correlation coefficient (12-lead ρ). Left ventricle (LV) vs. right ventricle (RV) SOO was estimated by computing the LV/RV ratio for each patient, obtained by dividing the average 12-lead ρ value of the LV- and RV-SOO simulated ECGs, respectively. Simulated ECGs that had virtual sites close to the ablation points that stopped the arrhythmia presented higher correlation coefficients. The LV/RV ratio correctly predicted LV vs. RV SOO in 10/11 cases; 1.07 vs. 0.93 P < 0.05 for 12-lead ρ. CONCLUSION: The obtained results demonstrate the potential of the developed in silico pace-mapping technique to complement standard ECG for the pre-operative planning of complex ventricular arrhythmias.

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts.

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum, and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modeling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modeling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.