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.

The Primary Alteration of Ventricular Myocardium Conduction: The Significant Determinant of Left Bundle Branch Block Pattern

Intraventricular conduction disturbances (IVCD) are currently generally accepted as ECG diagnostic categories. They are characterized by defined QRS complex patterns that reflect the abnormalities in the intraventricular sequence of activation that can be caused by pathology in the His-Purkinje conduction system (HP) or ventricular myocardium. However, the current understanding of the IVCD's underlying mechanism is mostly attributed to HP structural or functional alterations. The involvement of the working ventricular myocardium is only marginally mentioned or not considered. This opinion paper is focused on the alterations of the ventricular working myocardium leading to the most frequent IVCD pattern-the left bundle branch block pattern (LBBB). Recognizing the underlying mechanisms of the LBBB patterns and the involvement of the ventricular working myocardium is of utmost clinical importance, considering a patient's prognosis and indication for cardiac resynchronization therapy.

Vectorcardiography-derived index allows a robust quantification of ventricular electrical synchrony

Alteration of muscle activation sequence is a key mechanism in heart failure with reduced ejection fraction. Successful cardiac resynchronization therapy (CRT), which has become standard therapy in these patients, is limited by the lack of precise dyssynchrony quantification. We implemented a computational pipeline that allows assessment of ventricular dyssynchrony by vectorcardiogram reconstruction from the patient’s electrocardiogram. We defined a ventricular dyssynchrony index as the distance between the voltage and speed time integrals of an individual observation and the linear fit of these variables obtained from a healthy population. The pipeline was tested in a 1914-patient population. The dyssynchrony index showed minimum values in heathy controls and maximum values in patients with left bundle branch block (LBBB) or with a pacemaker (PM). We established a critical dyssynchrony index value that discriminates electrical dyssynchronous patterns (LBBB and PM) from ventricular synchrony. In 10 patients with PM or CRT devices, dyssynchrony indexes above the critical value were associated with high time to peak strain standard deviation, an echocardiographic measure of mechanical dyssynchrony. Our index proves to be a promising tool to evaluate ventricular activation dyssynchrony, potentially enhancing the selection of candidates for CRT, device configuration during implantation, and post-implant optimization.

Optimizing lead placement for pacing in dyssynchronous heart failure: The patient in the lead

Cardiac resynchronization therapy (CRT) greatly reduces morbidity and mortality in patients with dyssynchronous heart failure. However, despite tremendous efforts, response has been variable and can be further improved. Although optimizing left ventricular lead placement (LVLP) is arguably the cornerstone of CRT, the procedure of LVLP using the transvenous approach has remained largely unchanged for more than 2 decades. Improvements have been developed using scar location and electrical and/or mechanical mapping, and interest in conduction system pacing as an alternative to biventricular pacing has emerged recently. Conduction system pacing is promising but may not be suitable for all patients with dyssynchronous heart failure. This review underscores the importance of a patient-tailored approach and discusses the potential applications of both conduction system pacing and targeted biventricular CRT.

Using cardiac ionic cell models to interpret clinical data

For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of pacing leads to improve CRT response. We use a 3D electrophysiological computational model of the heart and torso to get insight into the changes in the activation patterns obtained when the heart is paced from different regions and for different atrioventricular and interventricular delays. The model represents a heart with left bundle branch block and heart failure, and allows a detailed and accurate analysis of the electrical changes observed simultaneously in the myocardium and in the QRS complex computed in the precordial leads. Computational simulations were performed using a modified version of the O'Hara et al. action potential model, the most recent mathematical model developed for human ventricular electrophysiology. The optimal location for the pacing leads was determined by QRS maximal reduction. Additionally, the influence of Purkinje system on CRT response was assessed and correlation analysis between several parameters of the QRS was made. Simulation results showed that the right ventricle (RV) upper septum near the outflow tract is an alternative location to the RV apical lead. Furthermore, LV endocardial pacing provided better results as compared to epicardial stimulation. Finally, the time to reach the 90% of the QRS area was a good predictor of the instant at which 90% of the ventricular tissue was activated. Thus, the time to reach the 90% of the QRS area is suggested as an additional index to assess CRT effectiveness to improve biventricular synchrony.

Cardiogoniometry can predict positive response to cardiac resynchronization therapy - A proof of concept study

Background

According to American Heart Association guidelines, QRS duration and morphology are used to select patients for cardiac resynchronization therapy (CRT). But still there are some patients who are not responding to this device. We investigated whether the Cardiogoniometry (CGM) as a three-dimensional vectorcardiogram method can improve patient selection.

Methods

Echocardiography and CGM were performed for 25 consecutive patients with Left bundle branch morphology who were candidate for CRT implantation and were in sinus rhythm. Patients re-evaluated by echocardiography after 6 months post CRT implantation.

Results

The mean age of the patients was 63 ± 13 years and 17 (68%) were males. The mean LVEF was 19.4 ± 7.4% and 24.2 ± 11.5% before and after CRT implantation respectively. Median of the duration of the R loop before the R maximum demonstrated a negative correlation with the increase in LVEF, (r = −0.36, P = 0.07) and mean of maximal spatial velocity of the T-loop for all measured showed a positive correlation (r = 0.39, p = 0.04). Other parameters didn't show any significant differences.

Conclusions

Three-dimensional vectorcardiogram parameters can be helpful to predict the CRT response. Shorter duration of the R loop before the maximum R and smaller R loop area are predictors for responder patients.

Computational Modeling for Cardiac Resynchronization Therapy

Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure (HF) patients with an electrical substrate pathology causing ventricular dyssynchrony. However 40-50% of patients do not respond to treatment. Cardiac modeling of the electrophysiology, electromechanics, and hemodynamics of the heart has been used to study mechanisms behind HF pathology and CRT response. Recently, multi-scale dyssynchronous HF models have been used to study optimal device settings and optimal lead locations, investigate the underlying cardiac pathophysiology, as well as investigate emerging technologies proposed to treat cardiac dyssynchrony. However the breadth of patient and experimental data required to create and parameterize these models and the computational resources required currently limits the use of these models to small patient numbers. In the future, once these technical challenges are overcome, biophysically based models of the heart have the potential to become a clinical tool to aid in the diagnosis and treatment of HF.

Implantable Cardioverter-Defibrillators With Versus Without Resynchronization Therapy in Patients With a QRS Duration >180 ms

BACKGROUND

More than 20% of Medicare beneficiaries receiving cardiac resynchronization therapy defibrillators (CRT-D) have a very wide (≥180 ms) QRS complex duration (QRSD). Outcomes of CRT-D in these patients are not well-established because they have been underrepresented in clinical trials.

OBJECTIVES

This study examined outcomes in patients with CRT-D in a very wide QRSD with left bundle branch block (LBBB) versus those without LBBB.

METHODS

Medicare patients from the Implantable Cardioverter Defibrillator Registry (January 1, 2005, through April 30, 2006) with a CRT-D and confirmed Class I or IIa indications for CRT-D were matched to implantable cardioverter-defibrillator (ICD) patients without CRT despite having Class I or IIa indications for CRT. Mortality and heart failure hospitalizations longer than 4 years with CRT-D versus standard ICDs based on a QRSD and morphology were analyzed.

RESULTS

We analyzed 24,960 patients. Among those with LBBB, patients with a QRSD ≥180 ms had a greater adjusted survival benefit with CRT-D versus standard ICD (hazard ration [HR] for death: 0.65; 95% confidence interval [CI]: 0.59 to 0.72) compared with those having a QRSD 120 to 149 ms (HR: 0.85; 95% CI: 0.80 to 0.92) and 150 to 179 ms (HR: 0.87; 95% CI: 0.81 to 0.93). CRT-D versus ICD was associated with an improvement in survival in those with LBBB and a QRSD ≥180 ms (adjusted HR for death: 0.78; 95% CI: 0.68 to 0.91), but not in those with LBBB and a QRSD 150 to 179 ms (adjusted HR for death: 1.06; 95% CI: 0.95 to 1.19).

CONCLUSIONS

Improvements in both survival and heart failure hospitalizations with CRT-D were greatest in patients with a QRSD ≥180 ms with or without LBBB, whereas patients with a QRSD 150 to 179 ms without LBBB had no improvement in survival with CRT-D, and those with a QRSD 150 to 179 ms and LBBB had only a modest improvement.

The 4th Report of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy

The 4th Report provides a brief review of publications focused on the electrocardiographic diagnosis of left ventricular hypertrophy published during the period of 2010 to 2016 by the members of the Working Group on ECG diagnosis of Left Ventricular Hypertrophy. The Working Group recommended that ECG research and clinical attention be redirected from the estimation of LVM to the identification of electrical remodeling, to better understanding the sequence of events connecting electrical remodeling to outcomes. The need for a re-definition of terms and for a new paradigm is also stressed.

Lessons learned from multi-scale modeling of the failing heart

Heart failure constitutes a major public health problem worldwide. Affected patients experience a number of changes in the electrical function of the heart that predispose to potentially lethal cardiac arrhythmias. Due to the multitude of electrophysiological changes that may occur during heart failure, the scientific literature is complex and sometimes ambiguous, perhaps because these findings are highly dependent on the etiology, the stage of heart failure, and the experimental model used to study these changes. Nevertheless, a number of common features of failing hearts have been documented. Prolongation of the action potential (AP) involving ion channel remodeling and alterations in calcium handling have been established as the hallmark characteristics of myocytes isolated from failing hearts. Intercellular uncoupling and fibrosis are identified as major arrhythmogenic factors. Multi-scale computational simulations are a powerful tool that complements experimental and clinical research. The development of biophysically detailed computer models of single myocytes and cardiac tissues has contributed greatly to our understanding of processes underlying excitation and repolarization in the heart. The electrical, structural, and metabolic remodeling that arises in cardiac tissues during heart failure has been addressed from different computational perspectives to further understand the arrhythmogenic substrate. This review summarizes the contributions from computational modeling and simulation to predict the underlying mechanisms of heart failure phenotypes and their implications for arrhythmogenesis, ranging from the cellular level to whole-heart simulations. The main aspects of heart failure are presented in several related sections. An overview of the main electrophysiological and structural changes that have been observed experimentally in failing hearts is followed by the description and discussion of the simulation work in this field at the cellular level, and then in 2D and 3D cardiac structures. The implications for arrhythmogenesis in heart failure are also discussed including therapeutic measures, such as drug effects and cardiac resynchronization therapy. Finally, the future challenges in heart failure modeling and simulation will be discussed.

T-wave area as biomarker of clinical response to cardiac resynchronization therapy

AIMS

There is increasing evidence that left bundle branch block (LBBB) morphology on the electrocardiogram is a positive predictor for response to cardiac resynchronization therapy (CRT). We previously demonstrated that the vectorcardiography (VCG)-derived T-wave area predicts echocardiographic CRT response in LBBB patients. In the present study, we investigate whether the T-wave area also predicts long-term clinical outcome to CRT.

METHODS AND RESULTS

This is a retrospective study consisting of 335 CRT recipients. Primary endpoint were the composite of heart failure (HF) hospitalization, heart transplantation, left ventricular assist device implantation or death during a 3-year follow-up period. HF hospitalization and death alone were secondary endpoints. The patient subgroup with a large T-wave area and LBBB 36% reached the primary endpoint, which was considerably less (P < 0.01) than for patients with LBBB and a small T-wave area or non-LBBB patients with a small or large T-wave area (48, 57, and 51%, respectively). Similar differences were observed for the secondary endpoints, HF hospitalization (31 vs. 51, 51, and 38%, respectively, P < 0.01) and death (19 vs. 42, 34, and 42%, respectively, P < 0.01). In multivariate analysis, a large T-wave area and LBBB were the only independent predictors of the combined endpoint besides high creatinine levels and use of diuretics.

CONCLUSION

T-wave area may be useful as an additional biomarker to stratify CRT candidates and improve selection of those most likely to benefit from CRT. A large T-wave area may derive its predictive value from reflecting good intrinsic myocardial properties and a substrate for CRT.

Electrocardiographic Predictors of Cardiovascular Mortality

Cardiovascular diseases are the main causes of mortality. Sudden cardiac death may also appear in athletes, due to underlying congenital or inherited cardiac abnormalities. The electrocardiogram is used in clinical practice and clinical trials, as a valid, reliable, accessible, inexpensive method. The aim of the present paper was to review electrocardiographic (ECG) signs associated with cardiovascular mortality and the mechanisms underlying those associations, providing a brief description of the main studies in this area, and consider their implication for clinical practice in the general population and athletes. The main ECG parameters associated with cardiovascular mortality in the present paper are the P wave (duration, interatrial block, and deep terminal negativity of the P wave in V1), prolonged QT and Tpeak-Tend intervals, QRS duration and fragmentation, bundle branch block, ST segment depression and elevation, T waves (inverted, T wave axes), spatial angles between QRS and T vectors, premature ventricular contractions, and ECG hypertrophy criteria.

Patient-specific modelling of cardiac electrophysiology in heart-failure patients

Aims

Left-ventricular (LV) conduction disturbances are common in heart-failure patients and a left bundle-branch block (LBBB) electrocardiogram (ECG) type is often seen. The precise cause of this pattern is uncertain and is probably variable between patients, ranging from proximal interruption of the left bundle branch to diffuse distal conduction disease in the working myocardium. Using realistic numerical simulation methods and patient-tailored model anatomies, we investigated different hypotheses to explain the observed activation order on the LV endocardium, electrogram morphologies, and ECG features in two patients with heart failure and LBBB ECG.

Methods and results

Ventricular electrical activity was simulated using reaction–diffusion models with patient-specific anatomies. From the simulated action potentials, ECGs and cardiac electrograms were computed by solving the bidomain equation. Model parameters such as earliest activation sites, tissue conductivity, and densities of ionic currents were tuned to reproduce the measured signals. Electrocardiogram morphology and activation order could be matched simultaneously. Local electrograms matched well at some sites, but overall the measured waveforms had deeper S-waves than the simulated waveforms.

Conclusion

Tuning a reaction–diffusion model of the human heart to reproduce measured ECGs and electrograms is feasible and may provide insights in individual disease characteristics that cannot be obtained by other means.

Computer Modelling for Better Diagnosis and Therapy of Patients by Cardiac Resynchronisation Therapy

Mathematical or computer models have become increasingly popular in biomedical science. Although they are a simplification of reality, computer models are able to link a multitude of processes to each other. In the fields of cardiac physiology and cardiology, models can be used to describe the combined activity of all ion channels (electrical models) or contraction-related processes (mechanical models) in potentially millions of cardiac cells. Electromechanical models go one step further by coupling electrical and mechanical processes and incorporating mechano-electrical feedback. The field of cardiac computer modelling is making rapid progress due to advances in research and the ever-increasing calculation power of computers. Computer models have helped to provide better understanding of disease mechanisms and treatment. The ultimate goal will be to create patient-specific models using diagnostic measurements from the individual patient. This paper gives a brief overview of computer models in the field of cardiology and mentions some scientific achievements and clinical applications, especially in relation to cardiac resynchronisation therapy (CRT).

Advances in modeling ventricular arrhythmias: from mechanisms to the clinic

Modern cardiovascular research has increasingly recognized that heart models and simulation can help interpret an array of experimental data and dissect important mechanisms and interrelationships, with developments rooted in the iterative interaction between modeling and experimentation. This article reviews the progress made in simulating cardiac electrical behavior at the level of the organ and, specifically, in the development of models of ventricular arrhythmias and fibrillation, as well as their termination (defibrillation). The ability to construct multiscale models of ventricular arrhythmias, representing integrative behavior from the molecule to the entire organ, has enabled mechanistic inquiry into the dynamics of ventricular arrhythmias in the diseased myocardium, in understanding drug-induced proarrhythmia, and in the development of new modalities for defibrillation, to name a few. In this article, we also review the initial use of ventricular models of arrhythmia in personalized diagnosis, treatment planning, and prevention of sudden cardiac death. Implementing individualized cardiac simulations at the patient bedside is poised to become one of the most thrilling examples of computational science and engineering approaches in translational medicine.