Translating myosin-binding protein C and titin abnormalities to whole-heart function using a novel calcium-contraction coupling model.

Mutations in cardiac myosin-binding protein C (cMyBP-C) or titin may respectively lead to hypertrophic (HCM) or dilated (DCM) cardiomyopathies. The mechanisms leading to these phenotypes remain unclear because of the challenge of translating cellular abnormalities to whole-heart and system function. We developed and validated a novel computer model of calcium-contraction coupling incorporating the role of cMyBP-C and titin based on the key assumptions: 1) tension in the thick filament promotes cross-bridge attachment mechanochemically, 2) with increasing titin tension, more myosin heads are unlocked for attachment, and 3) cMyBP-C suppresses cross-bridge attachment. Simulated stationary calcium-tension curves, isotonic and isometric contractions, and quick release agreed with experimental data. The model predicted that a loss of cMyBP-C function decreases the steepness of the calcium-tension curve, and that more compliant titin decreases the level of passive and active tension and its dependency on sarcomere length. Integrating this cellular model in the CircAdapt model of the human heart and circulation showed that a loss of cMyBP-C function resulted in HCM-like hemodynamics with higher left ventricular end-diastolic pressures and smaller volumes. More compliant titin led to higher diastolic pressures and ventricular dilation, suggesting DCM-like hemodynamics. The novel model of calcium-contraction coupling incorporates the role of cMyBP-C and titin. Its coupling to whole-heart mechanics translates changes in cellular calcium-contraction coupling to changes in cardiac pump and circulatory function and identifies potential mechanisms by which cMyBP-C and titin abnormalities may develop into HCM and DCM phenotypes. This modeling platform may help identify distinct mechanisms underlying clinical phenotypes in cardiac diseases.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Increased Chamber Resting Tone Is a Key Determinant of Left Ventricular Diastolic Dysfunction.

BACKGROUND: Twitch-independent tension has been demonstrated in cardiomyocytes, but its role in heart failure (HF) is unclear. We aimed to address twitch-independent tension as a source of diastolic dysfunction by isolating the effects of chamber resting tone (RT) from impaired relaxation and stiffness. METHODS: We invasively monitored pressure-volume data during cardiopulmonary exercise in 20 patients with hypertrophic cardiomyopathy, 17 control subjects, and 35 patients with HF with preserved ejection fraction. To measure RT, we developed a new method to fit continuous pressure-volume measurements, and first validated it in a computational model of loss of cMyBP-C (myosin binding protein-C). RESULTS: In hypertrophic cardiomyopathy, RT (estimated marginal mean [95% CI]) was 3.4 (0.4-6.4) mm Hg, increasing to 18.5 (15.5-21.5) mm Hg with exercise (P<0.001). At peak exercise, RT was responsible for 64% (53%-76%) of end-diastolic pressure, whereas incomplete relaxation and stiffness accounted for the rest. RT correlated with the levels of NT-proBNP (N-terminal pro-B-type natriuretic peptide; R=0.57; P=0.02) and with pulmonary wedge pressure but following different slopes at rest and during exercise (R2=0.49; P<0.001). In controls, RT was 0.0 mm Hg and 1.2 (0.3-2.8) mm Hg in HF with preserved ejection fraction patients and was also exacerbated by exercise. In silico, RT increased in parallel to the loss of cMyBP-C function and correlated with twitch-independent myofilament tension (R=0.997). CONCLUSIONS: Augmented RT is the major cause of LV diastolic chamber dysfunction in hypertrophic cardiomyopathy and HF with preserved ejection fraction. RT transients determine diastolic pressures, pulmonary pressures, and functional capacity to a greater extent than relaxation and stiffness abnormalities. These findings support antimyosin agents for treating HF.

Electrophysiological effects of stretch-activated ion channels: a systematic computational characterization.

Cardiac electrophysiology and mechanics are strongly interconnected. Their interaction is, among others, mediated by mechano-electric feedback through stretch-activated ion channels (SACs). The electrophysiological changes induced by SACs may contribute to arrhythmogenesis, but the precise SAC-induced electrophysiological changes remain incompletely understood. Here, we provide a systematic characterization of stretch effects through three distinguished SACs on cardiac electrophysiology using computational modelling. We implemented potassium-selective, calcium-selective and non-selective SACs in the Tomek-Rodriguez-O'Hara-Rudy model of human ventricular electrophysiology. The model was calibrated to experimental data from isolated cardiomyocytes undergoing stretch, considering inter-species differences, and disease-related remodelling of SACs. SAC-mediated effects on the action potential (AP) were analysed by varying stretch amplitude, application timing and/or duration. Afterdepolarizations of different amplitudes were observed with transient 10-ms stretch stimuli of 15-18% applied during phase 4, while stretch ≥18% during phase 4 elicited triggered APs. Longer stimuli shifted the threshold of AP trigger during phase 4 to lower amplitudes, while shorter stimuli increased it. Continuous stretch provoked electrophysiological remodelling. Furthermore, stretch shortened duration or changed morphology of a subsequent electrically evoked AP, and, if applied during a vulnerable time window with sufficient amplitude, prevented its occurrence because of stretch-induced modulation of sodium and L-type calcium channel gating. These effects were more pronounced with disease-related SAC remodelling due to increased stretch sensitivity of diseased hearts. We showed that SACs may induce afterdepolarizations and triggered activities, and prevent subsequent AP generation or change its morphology. These effects depend on cardiomyocyte stretch characteristics and disease-related SACs remodelling and may contribute to cardiac arrhythmogenesis. KEY POINTS: The interplay between cardiac electrophysiology and mechanics is mediated by mechano-electric feedback through stretch-activated ion channels (SACs). These channels may be pro-arrhythmic, but their precise effect on electrophysiology remains unclear. Here we present a systematic in silico characterization of stretch effects through three SACs by implementing inter-species differences as well as disease-related remodelling of SACs in a novel computational model of human ventricular cardiomyocyte electrophysiology. Our simulations showed that, at the cellular level, SACs may provoke electrophysiological remodelling, afterdepolarizations, triggered activities, change the morphology or shorten subsequent electrically evoked action potentials. The model further suggests that a vulnerable window exists in which stretch prevents the following electrically triggered beat occurrence. The pro-arrhythmic effects of stretch strongly depend on disease-related SAC remodelling as well as on stretch characteristics, such as amplitude, time of application and duration. Our study helps in understanding the role of stretch in cardiac arrhythmogenesis and revealing the underlying cellular mechanisms.

Impact of Estimated Left Atrial Pressure on Cardiac Resynchronization Therapy Outcome.

BACKGROUND: We investigated the impact of baseline left atrial (LA) strain data and estimated left atrial pressure (LAP) by applying the 2016 American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines on cardiac resynchronization therapy (CRT) outcomes. METHODS: Datasets of 219 CRT patients were retrospectively analysed. All patients had full echocardiographic diastolic function assessment before CRT and were classified based on the guideline algorithm into normal LAP (nLAP = 40%), elevated LAP (eLAP = 49%) and indeterminate LAP (iLAP = 11%). All relevant baseline characteristics were analysed. CRT-induced left ventricular (LV) reverse remodeling was measured as the relative change of LV end-systolic volume (LVESV) at 12 ± 6 months after CRT compared to baseline. Patients were followed up for all-cause mortality for a mean of 4.8 years [interquartile range (IQR): 2.7-6.0 years]. RESULTS: At follow-up, CRT resulted in more pronounced reduction of LVESV in patients with nLAP than in patients with eLAP. In univariate analysis, nLAP was associated with LV reverse remodelling (p < 0.001), as well as long-term survival after CRT (p < 0.01). However, multivariable analysis showed that only the association between nLAP and LV reverse remodelling after CRT is independent (p < 0.01). Adding LA strain analysis to the guideline algorithm improved the feasibility of LAP estimation without affecting the association between estimated LAP and CRT outcome. CONCLUSION: Normal LAP before CRT, estimated using the 2016 ASE/EACVI guideline algorithm, is associated with LV reverse remodelling and long-term survival after CRT. Albeit non-independent, it can serve as a non-invasive imaging-based predictor of effective therapy. Furthermore, the inclusion of LA reservoir strain in the guideline algorithm can enhance the feasibility of LAP estimation without affecting the association between LAP and CRT outcome.

Association between comorbidities and left and right atrial dysfunction in patients with paroxysmal atrial fibrillation: Analysis of AF-RISK.

BACKGROUND: To identify the association between comorbidities and left atrial (LA) and right atrial (RA) function in patients with paroxysmal atrial fibrillation (AF). METHODS: This is a cross-sectional study. Speckle-tracking echocardiography was performed in 344 patients with paroxysmal AF at baseline, and available in 298 patients after 1-year follow-up. The number of comorbidities (hypertension, diabetes mellitus, coronary artery disease, body mass index > 25 kg/m2, age > 65 years, moderate to severe mitral valve regurgitation and kidney dysfunction (estimated glomerular filtration rate < 60 ml/min/1.73 m2)) was determined and the association with atrial strain was tested. RESULTS: Mean age of the patients was 58 (SD 12) years and 137 patients were women (40%). Patients with a higher number of comorbidities had larger LA volumes (p for trend <0.001), and had a decrease in all strain phases from the LA and RA, except for the RA contraction phase (p for trend 0.47). A higher number of comorbidities was associated with LA reservoir and conduit strain decrease independently of LA volume (p < 0.001, p < 0.001 respectively). Patients with 1-2 comorbidities, but not patients with 3 or more comorbidities, showed a further progression of impaired LA and RA function in almost all atrial strain phases at 14 [13-17] months follow-up. CONCLUSIONS: In patients with paroxysmal AF, individual and combined comorbidities are related to lower LA and RA strain. In patients with few comorbidities, impairment in atrial function progresses during one year of follow-up. Whether comorbidity management prevents or reverses decrease in atrial function warrants further study.

Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.

Introduction: Computational models of the cardiovascular system are widely used to simulate cardiac (dys)function. Personalization of such models for patient-specific simulation of cardiac function remains challenging. Measurement uncertainty affects accuracy of parameter estimations. In this study, we present a methodology for patient-specific estimation and uncertainty quantification of parameters in the closed-loop CircAdapt model of the human heart and circulation using echocardiographic deformation imaging. Based on patient-specific estimated parameters we aim to reveal the mechanical substrate underlying deformation abnormalities in patients with arrhythmogenic cardiomyopathy (AC). Methods: We used adaptive multiple importance sampling to estimate the posterior distribution of regional myocardial tissue properties. This methodology is implemented in the CircAdapt cardiovascular modeling platform and applied to estimate active and passive tissue properties underlying regional deformation patterns, left ventricular volumes, and right ventricular diameter. First, we tested the accuracy of this method and its inter- and intraobserver variability using nine datasets obtained in AC patients. Second, we tested the trueness of the estimation using nine in silico generated virtual patient datasets representative for various stages of AC. Finally, we applied this method to two longitudinal series of echocardiograms of two pathogenic mutation carriers without established myocardial disease at baseline. Results: Tissue characteristics of virtual patients were accurately estimated with a highest density interval containing the true parameter value of 9% (95% CI [0-79]). Variances of estimated posterior distributions in patient data and virtual data were comparable, supporting the reliability of the patient estimations. Estimations were highly reproducible with an overlap in posterior distributions of 89.9% (95% CI [60.1-95.9]). Clinically measured deformation, ejection fraction, and end-diastolic volume were accurately simulated. In presence of worsening of deformation over time, estimated tissue properties also revealed functional deterioration. Conclusion: This method facilitates patient-specific simulation-based estimation of regional ventricular tissue properties from non-invasive imaging data, taking into account both measurement and model uncertainties. Two proof-of-principle case studies suggested that this cardiac digital twin technology enables quantitative monitoring of AC disease progression in early stages of disease.

In silico Identification of Disrupted Myocardial Calcium Homeostasis as Proarrhythmic Trigger in Arrhythmogenic Cardiomyopathy.

Background: Patients with arrhythmogenic cardiomyopathy may suffer from lethal ventricular arrhythmias. Arrhythmogenic cardiomyopathy is predominantly triggered by mutations in plakophilin-2, a key component of cell-to-cell adhesion and calcium cycling regulation in cardiomyocytes. Calcium dysregulation due to plakophilin-2 mutations may lead to arrhythmias but the underlying pro-arrhythmic mechanisms remain unclear. Aim: To unravel the mechanisms by which calcium-handling abnormalities in plakophilin-2 loss-of-function may contribute to proarrhythmic events in arrhythmogenic cardiomyopathy. Methods: We adapted a computer model of mouse ventricular electrophysiology using recent experimental calcium-handling data from plakophilin-2 conditional knock-out (PKP2-cKO) mice. We simulated individual effects of beta-adrenergic stimulation, modifications in connexin43-mediated calcium entry, sodium-calcium exchanger (NCX) activity and ryanodine-receptor 2 (RyR2) calcium affinity on cellular electrophysiology and occurrence of arrhythmogenic events (delayed-afterdepolarizations). A population-of-models approach was used to investigate the generalizability of our findings. Finally, we assessed the potential translation of proposed mechanisms to humans, using a human ventricular cardiomyocyte computational model. Results: The model robustly reproduced the experimental calcium-handling changes in PKP2-cKO cardiomyocytes: an increased calcium transient amplitude (562 vs. 383 nM), increased diastolic calcium (120 vs. 91 nM), reduced L-type calcium current (15.0 vs. 21.4 pA/pF) and an increased free SR calcium (0.69 vs. 0.50 mM). Under beta-adrenergic stimulation, PKP2-cKO models from the population of models (n = 61) showed a higher susceptibility to delayed-afterdepolarizations compared to control (41 vs. 3.3%). Increased connexin43-mediated calcium entry further elevated the number of delayed-afterdepolarizations (78.7%, 2.5-fold increase in background calcium influx). Elevated diastolic cleft calcium appeared responsible for the increased RyR2-mediated calcium leak, promoting delayed-afterdepolarizations occurrence. A reduction in RyR2 calcium affinity prevented delayed-afterdepolarizations in PKP2-cKO models (24.6 vs. 41%). An additional increase in INCX strongly reduced delayed-afterdepolarizations occurrence, by lowering diastolic cleft calcium levels. The human model showed similar outcomes, suggesting a potential translational value of these findings. Conclusion: Beta-adrenergic stimulation and connexin43-mediated calcium entry upon loss of plakophilin-2 function contribute to generation of delayed-afterdepolarizations. RyR2 and NCX dysregulation play a key role in modulating these proarrhythmic events. This work provides insights into potential future antiarrhythmic strategies in arrhythmogenic cardiomyopathy due to plakophilin-2 loss-of-function.

The prognostic impact of mechanical atrial dysfunction and atrial fibrillation in heart failure with preserved ejection fraction.

AIMS: This study assessed the prognostic implications of mechanical atrial dysfunction in heart failure with preserved ejection fraction (HFpEF) patients with different stages of atrial fibrillation (AF) in detail. METHODS AND RESULTS: HFpEF patients (n = 258) systemically underwent an extensive clinical characterization, including 24-h Holter monitoring and speckle-tracking echocardiography. Patients were categorized according to rhythm and stages of AF: 112 with no history of AF (no AF), 56 with paroxysmal AF (PAF), and 90 with sustained (persistent/permanent) AF (SAF). A progressive decrease in mechanical atrial function was seen: left atrial reservoir strain (LASr) 30.5 ± 10.5% (no AF), 22.3 ± 10.5% (PAF), and 13.9 ± 7.8% (SAF), P < 0.001. Independent predictors for lower LASr values were AF, absence of chronic obstructive pulmonary disease, higher N-terminal-pro hormone B-type natriuretic peptide, left atrial volume index, and relative wall thickness, lower left ventricular global longitudinal strain, and echocardiographic signs of elevated left ventricular filling pressure. LASr was an independent predictor of adverse outcome (hazard ratio per 1% decrease =1.049, 95% confidence interval 1.014-1.085, P = 0.006), whereas AF was not when the multivariable model included LASr. Moreover, LASr mediated the adverse outcome associated with AF in HFpEF (P = 0.008). CONCLUSION: Mechanical atrial dysfunction has a possible greater prognostic role in HFpEF compared to AF status alone. Mechanical atrial dysfunction is a predictor of adverse outcome independently of AF presence or stage, and may be an underlying mechanism (mediator) for the worse outcome associated with AF in HFpEF. This may suggest mechanical atrial dysfunction plays a crucial role in disease progression in HFpEF patients with AF, and possibly also in HFpEF patients without AF.

Both beat-to-beat changes in RR-interval and left ventricular filling time determine ventricular function during atrial fibrillation.

AIMS: The irregular atrial electrical activity during atrial fibrillation (AF) is associated with a variable left ventricular (LV) systolic function. The mechanisms determining LV function during AF remain incompletely understood. We aimed at elucidating how changes in RR-interval and LV preload affect LV function during AF. METHODS AND RESULTS: Beat-to-beat speckle-tracking echocardiography was performed in 10 persistent AF patients. We evaluated the relation between longitudinal LV peak strain and preceding RR-interval during AF. We used the CircAdapt computational model to evaluate beat-to-beat preload and peak strain during AF for each patient by imposing the patient-specific RR-interval sequences and a non-contractile atrial myocardium. Generic simulations with artificial RR-interval sequences quantified the haemodynamic changes induced by sudden irregular beats. Clinical data and simulations both showed a larger sensitivity of peak strain to changes in preceding RR-interval at slow heart rate (HR) (cycle length, CL <750 ms) than at faster HR. Simulations explained this by a difference in preload of the current beat. Generic simulations confirmed a larger sensitivity of peak strain to preceding RR-interval at fast HR (CL = 600 ms: Δ peak strain = 3.7% vs. 900 ms: Δ peak strain = 0.3%) as in the patients. They suggested that longer LV activation with respect to preceding RR-interval is determinant for this sensitivity. CONCLUSIONS: During AF, longitudinal LV peak strain is highly variable, particularly at fast HR. Beat-to-beat changes in preload explain the differences in LV systolic function. Simulations revealed that a reduced diastolic LV filling time can explain the increased variability at fast HR.

Electromechanical substrate characterization in arrhythmogenic cardiomyopathy using imaging-based patient-specific computer simulations.

AIMS: Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. The aim of this study is to use computer simulations to non-invasively estimate the individual patient's myocardial tissue substrates underlying regional right ventricular (RV) deformation abnormalities in a cohort of AC mutation carriers. METHODS AND RESULTS: In 68 AC mutation carriers and 20 control subjects, regional longitudinal deformation patterns of the RV free wall (RVfw), interventricular septum (IVS), and left ventricular free wall (LVfw) were obtained using speckle-tracking echocardiography. We developed and used a patient-specific parameter estimation protocol based on the multi-scale CircAdapt cardiovascular system model to create virtual AC subjects. Using the individual's deformation data as model input, this protocol automatically estimated regional RVfw and global IVS and LVfw tissue properties. The computational model was able to reproduce clinically measured regional deformation patterns for all subjects, with highly reproducible parameter estimations. Simulations revealed that regional RVfw heterogeneity of both contractile function and compliance were increased in subjects with clinically advanced disease compared to mutation carriers without clinically established disease (17 ± 13% vs. 8 ± 4%, P = 0.01 and 18 ± 11% vs. 10 ± 7%, P < 0.01, respectively). No significant difference in activation delay was found. CONCLUSION: Regional RV deformation abnormalities in AC mutation carriers were related to reduced regional contractile function and tissue compliance. In clinically advanced disease stages, a characteristic apex-to-base heterogeneity of tissue abnormalities was present in the majority of the subjects, with most pronounced disease in the basal region of the RVfw.

Differentiating the effects of ß-adrenergic stimulation and stretch on calcium and force dynamics using a novel electromechanical cardiomyocyte model.

Cardiac electrophysiology and mechanics are strongly interconnected. Calcium is crucial in this complex interplay through its role in cellular electrophysiology and sarcomere contraction. We aim to differentiate the effects of acute ß-adrenergic stimulation (ß-ARS) and cardiomyocyte stretch (increased sarcomere length) on calcium-transient dynamics and force generation, using a novel computational model of cardiac electromechanics. We implemented a bidirectional coupling between the O'Hara-Rudy model of human ventricular electrophysiology and the MechChem model of sarcomere mechanics through the buffering of calcium by troponin. The coupled model was validated using experimental data from large mammals or human samples. Calcium transient and force were simulated for various degrees of ß-ARS and initial sarcomere lengths. The model reproduced force-frequency, quick-release, and isotonic contraction experiments, validating the bidirectional electromechanical interactions. An increase in ß-ARS increased the amplitudes of force (augmented inotropy) and calcium transient, and shortened both force and calcium-transient duration (lusitropy). An increase in sarcomere length increased force amplitude even more, but decreased calcium-transient amplitude and increased both force and calcium-transient duration. Finally, a gradient in relaxation along the thin filament may explain the nonmonotonic decay in cytosolic calcium observed with high tension. Using a novel coupled human electromechanical model, we identified differential effects of ß-ARS and stretch on calcium and force. Stretch mostly contributed to increased force amplitude and ß-ARS to the reduction of calcium and force duration. We showed that their combination, rather than individual contributions, is key to ensure force generation, rapid relaxation, and low diastolic calcium levels.NEW & NOTEWORTHY This work identifies the contribution of electrical and mechanical alterations to regulation of calcium and force under exercise-like conditions using a novel human electromechanical model integrating ventricular electrophysiology and sarcomere mechanics. By better understanding their individual and combined effects, this can uncover arrhythmogenic mechanisms in exercise-like situations. This publicly available model is a crucial step toward understanding the complex interplay between cardiac electrophysiology and mechanics to improve arrhythmia risk prediction and treatment.

Parameter subset reduction for patient-specific modelling of arrhythmogenic cardiomyopathy-related mutation carriers in the CircAdapt model.

Arrhythmogenic cardiomyopathy (AC) is an inherited cardiac disease, clinically characterized by life-threatening ventricular arrhythmias and progressive cardiac dysfunction. Patient-specific computational models could help understand the disease progression and may help in clinical decision-making. We propose an inverse modelling approach using the CircAdapt model to estimate patient-specific regional abnormalities in tissue properties in AC subjects. However, the number of parameters (n = 110) and their complex interactions make personalized parameter estimation challenging. The goal of this study is to develop a framework for parameter reduction and estimation combining Morris screening, quasi-Monte Carlo (qMC) simulations and particle swarm optimization (PSO). This framework identifies the best subset of tissue properties based on clinical measurements allowing patient-specific identification of right ventricular tissue abnormalities. We applied this framework on 15 AC genotype-positive subjects with varying degrees of myocardial disease. Cohort studies have shown that atypical regional right ventricular (RV) deformation patterns reveal an early-stage AC disease. The CircAdapt model of cardiovascular mechanics and haemodynamics has already demonstrated its ability to capture typical deformation patterns of AC subjects. We, therefore, use clinically measured cardiac deformation patterns to estimate model parameters describing myocardial disease substrates underlying these AC-related RV deformation abnormalities. Morris screening reduced the subset to 48 parameters. qMC and PSO further reduced the subset to a final selection of 16 parameters, including regional tissue contractility, passive stiffness, activation delay and wall reference area. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Cardiomyocyte calcium handling in health and disease: Insights from in vitro and in silico studies.

Calcium (Ca2+) plays a central role in cardiomyocyte excitation-contraction coupling. To ensure an optimal electrical impulse propagation and cardiac contraction, Ca2+ levels are regulated by a variety of Ca2+-handling proteins. In turn, Ca2+ modulates numerous electrophysiological processes. Accordingly, Ca2+-handling abnormalities can promote cardiac arrhythmias via various mechanisms, including the promotion of afterdepolarizations, ion-channel modulation and structural remodeling. In the last 30 years, significant improvements have been made in the computational modeling of cardiomyocyte Ca2+ handling under physiological and pathological conditions. However, numerous questions involving the Ca2+-dependent regulation of different macromolecular complexes, cross-talk between Ca2+-dependent regulatory pathways operating over a wide range of time scales, and bidirectional interactions between electrophysiology and mechanics remain to be addressed by in vitro and in silico studies. A better understanding of disease-specific Ca2+-dependent proarrhythmic mechanisms may facilitate the development of improved therapeutic strategies. In this review, we describe the fundamental mechanisms of cardiomyocyte Ca2+ handling in health and disease, and provide an overview of currently available computational models for cardiomyocyte Ca2+ handling. Finally, we discuss important uncertainties and open questions about cardiomyocyte Ca2+ handling and highlight how synergy between in vitro and in silico studies may help to answer several of these issues.

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis. OBJECTIVE: The purpose of this study was to improve LQTS diagnosis by combining new polynomial-based T-wave morphology parameters with the corrected QT interval (QTc), age, and sex in a model. METHODS: A retrospective cohort consisting of 333 patients with LQTS and 345 genotype-negative family members was used in this study. For each patient, a linear combination of the first 2 Hermite-Gauss (HG) polynomials was fitted to the STT segments of an average complex of all precordial leads and limb leads I and II. The weight coefficients as well as the error of the best fit were used to characterize T-wave morphology. Subjects were classified as patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine models fed with different features. An external cohort consisting of 72 patients and 45 controls was finally used to check the robustness of the models. RESULTS: Baseline QTc cutoffs were specific but had low sensitivity in diagnosing LQTS. The model with T-wave morphology features, QTc, age, and sex had the best overall accuracy (84%), followed by a model with QTc, age, and sex (79%). The model with T-wave morphology features especially performed better in LQTS type 3 patients (69%). CONCLUSION: T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis.

Machine learning in the electrocardiogram.

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge increase of electronic health records containing a systematised collection of different type of digitalised medical data, together with new tools to analyse this large amount of data in an efficient way have re-emerged the field of machine learning in healthcare innovation. This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques. Machine learning, including deep learning, have shown to be powerful tools for aiding clinicians in patient screening and risk stratification tasks. However, they do not provide the physiological basis of classification outcomes. Computational modelling and simulation can help in the interpretation and understanding of key physiologically meaningful ECG biomarkers extracted from machine learning techniques.

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities.

AIMS: To identify key structural and electrophysiological features explaining distinct electrocardiogram (ECG) phenotypes in hypertrophic cardiomyopathy (HCM). METHODS AND RESULTS: Human heart-torso anatomical models were constructed from cardiac magnetic resonance (CMR) images of HCM patients, representative of ECG phenotypes identified previously. High performance computing simulations using bidomain models were conducted to dissect key features explaining the ECG phenotypes with increased HCM Risk-SCD scores, namely Group 1A, characterized by normal QRS but inverted T waves laterally and coexistence of apical and septal hypertrophy; and Group 3 with marked QRS abnormalities (deep and wide S waves laterally) and septal hypertrophy. Hypertrophic cardiomyopathy abnormalities characterized from CMR, such as hypertrophy, tissue microstructure alterations, abnormal conduction system, and ionic remodelling, were selectively included to assess their influence on ECG morphology. Electrocardiogram abnormalities could not be explained by increased wall thickness nor by local conduction abnormalities associated with fibre disarray or fibrosis. Inverted T wave with normal QRS (Group 1A) was obtained with increased apico-basal repolarization gradient caused by ionic remodelling in septum and apex. Lateral QRS abnormalities (Group 3) were only recovered with abnormal Purkinje-myocardium coupling. CONCLUSION: Two ECG-based HCM phenotypes are explained by distinct mechanisms: ionic remodelling and action potential prolongation in hypertrophied apical and septal areas lead to T wave inversion with normal QRS complexes, whereas abnormal Purkinje-myocardial coupling causes abnormal QRS morphology in V4-V6. These findings have potential implications for patients' management as they point towards different arrhythmia mechanisms in different phenotypes.

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging. Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups. Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively). Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.