Ganglionated plexi stimulation induces pulmonary vein triggers and promotes atrial arrhythmogenecity: In silico modeling study

Autonomic modulation impacts conduction velocity dynamics and wavefront propagation in the left atrium

AIMS

Atrial fibrosis and autonomic remodelling are proposed pathophysiological mechanisms in atrial fibrillation (AF). Their impact on conduction velocity (CV) dynamics and wavefront propagation was evaluated.

METHODS AND RESULTS

Local activation times (LATs), voltage, and geometry data were obtained from patients undergoing ablation for persistent AF. LATs were obtained at three pacing intervals (PIs) in sinus rhythm (SR). LATs were used to determine CV dynamics and their relationship to local voltage amplitude. The impact of autonomic modulation- pharmacologically and with ganglionated plexi (GP) stimulation, on CV dynamics, wavefront propagation, and pivot points (change in wavefront propagation of ≥90°) was determined in SR. Fifty-four patients were included. Voltage impacted CV dynamics whereby at non-low voltage zones (LVZs) (≥0.5 mV) the CV restitution curves are steeper [0.03 ± 0.03 m/s ΔCV PI 600-400 ms (PI1), 0.54 ± 0.09 m/s ΔCV PI 400-250 ms (PI2)], broader at LVZ (0.2-0.49 mV) (0.17 ± 0.09 m/s ΔCV PI1, 0.25 ± 0.11 m/s ΔCV PI2), and flat at very LVZ (<0.2 mV) (0.03 ± 0.01 m/s ΔCV PI1, 0.04 ± 0.02 m/s ΔCV PI2). Atropine did not change CV dynamics, while isoprenaline and GP stimulation resulted in greater CV slowing with rate. Isoprenaline (2.7 ± 1.1 increase/patient) and GP stimulation (2.8 ± 1.3 increase/patient) promoted CV heterogeneity, i.e. rate-dependent CV (RDCV) slowing sites. Most pivot points co-located to RDCV slowing sites (80.2%). Isoprenaline (1.3 ± 1.1 pivot increase/patient) and GP stimulation (1.5 ± 1.1 increase/patient) also enhanced the number of pivot points identified.

CONCLUSION

Atrial CV dynamics is affected by fibrosis burden and influenced by autonomic modulation which enhances CV heterogeneity and distribution of pivot points. This study provides further insight into the impact of autonomic remodelling in AF.

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.

Prognostic value of heart rate variability in atrial fibrillation recurrence following catheter ablation: A systematic review and meta-analysis

Background

Atrial fibrillation (AF) has been a worldwide health issue with increasing prevalence and mortality. Recently, increasing attention has been gained to the relationship between heart rate variability (HRV) and the clinical prognosis of AF catheter ablation. We aimed to evaluate the prognostic value of HRV in AF recurrence.

Methods

We systematically searched Web of Science, PubMed, and Embase from inception until 17 August 2022 to conduct the systematic review and meta-analysis. We included the studies reporting the predictive value of HRV parameters for AF recurrence or in which HRV parameters in AF recurrence and non-recurrence groups were individually reported.

Results

Finally, we enrolled 16 studies, including 2,352 patients. Higher rMSSD could independently predict AF recurrence following catheter ablation (OR: 1.02, 95% CI: 1.00-1.04; p = 0.03). Higher HF (OR: 1.55, 95% CI: 1.05-2.28; p = 0.03) and lower LF/HF (OR: 1.12, 95% CI: 1.03-1.20; p = 0.004) could independently predict AF recurrence within 1 year. Higher SDNN (OR: 1.02, 95% CI: 101-1.02; p = 0.0006) could independently predict AF recurrence among patients with paroxysmal AF. Almost all HRV parameters within 3 days after catheter ablation and lnHF, lnLF, and rMSSD at 3 months after catheter ablation performed significant differences in AF recurrence and non-recurrence groups.

Conclusion

Heart rate variability, especially higher rMSSD (within short-term and long-term periods), was closely related to recurrent AF following catheter ablation, highlighting the clinical importance of HRV in the prognosis of AF following catheter ablation.

Arrhythmogenic influence of mutations in a myocyte-based computational model of the pulmonary vein sleeve

In the heart, electrophysiological dysregulation arises from defects at many biological levels (from point mutations in ion channel proteins to gross structural abnormalities). These defects disrupt the normal pattern of electrical activation, producing ectopic activity and reentrant arrhythmia. To interrogate mechanisms that link these primary biological defects to macroscopic electrophysiologic dysregulation most prior computational studies have utilized either (i) detailed models of myocyte ion channel dynamics at limited spatial scales, or (ii) homogenized models of action potential conduction that reproduce arrhythmic activity at tissue and organ levels. Here we apply our recent model (EMI), which integrates electrical activation and propagation across these scales, to study human atrial arrhythmias originating in the pulmonary vein (PV) sleeves. These small structures initiate most supraventricular arrhythmias and include pronounced myocyte-to-myocyte heterogeneities in ion channel expression and intercellular coupling. To test EMI's cell-based architecture in this physiological context we asked whether ion channel mutations known to underlie atrial fibrillation are capable of initiating arrhythmogenic behavior via increased excitability or reentry in a schematic PV sleeve geometry. Our results illustrate that EMI's improved spatial resolution can directly interrogate how electrophysiological changes at the individual myocyte level manifest in tissue and as arrhythmia in the PV sleeve.

Computational modeling of atrial fibrillation

With the aging society, the prevalence of atrial fibrillation (AF) continues to increase. Nevertheless, there are still limitations in antiarrhythmic drugs (AAD) or catheter interventions for AF. If it is possible to predict the outcome of AF management according to various AADs or ablation lesion sets through computational modeling, it will be of great clinical help. AF computational modeling has been utilized for in-silico arrhythmia research and enabled high-density entire chamber mapping, reproducible condition control, virtual intervention, not possible clinically or experimentally, in-depth mechanistic research. With the recent development of computer science and technology, more sophisticated and faster computational modeling has become available for clinical application. In particular, it can be applied to determine the extra-PV target of persistent AF catheter ablation or to select the AAD with the best effect. AF computational modeling combined with artificial intelligence is expected to contribute to precision medicine for more diverse uses in the future. Therefore, in this review, we will deal with the history, development, and various applications of computation modeling.

[Safety and Medium- and Long-Term Efficacy of Treating Atrial Fibrillation with Surgical Radiofrequency Ablation during Concomitant Mitral Valve Surgery]

OBJECTIVE

To investigate the safety and medium- and long-term efficacy of surgical radiofrequency ablation to treat atrial fibrillation during concomitant mitral valve surgery.

METHODS

From January 2014 to December 2018, 280 patients with mitral valve disease and the comorbidity of preoperative atrial fibrillation were recruited for the study. Among them, 130 patients received only mitral valve surgery (non-ablation group), and 150 patients were underwent surgical radiofrequency ablation for the atrial fibrillation during concomitant mitral valve surgery (ablation group). Among the 150 patients of the ablation group, 80 had biatrial ablation, and 70 had left atrial ablation. Under general anesthesia, median sternotomy was done on all patients and cardiopulmonary bypass was established through the ascending aorta and superior and inferior venae cavae. After aortic occlusion, patients in the ablation group underwent the ablation procedure with the Atricure® bipolar ablation device, using the Cox Maze Ⅲ procedure as a reference. In addition, the left atrial appendage was removed and electrocautery of the ligament of Marshall and Waterston's groove were performed in all Cox Maze cases. Following ablation, mitral valve replacement or repair was performed. All patients were given 200 mg oral amiodarone on the first day after surgery, for three times/d×7 d, which was followed by twice/d×7 d, and then oral amiodarone 200 mg/d was maintained till the end of 3 months after surgery (ablation group) or 12 months after surgery (non-ablation group). Patients were followed up at the intervals of 3 months, 6 months, 12 months, 2 years, 3 years, and 5 years after discharge. The follow-up service included standard 12-lead chest electrocardiogram (ECG) and 24-h dynamic ECG. The primary end point of the study was the time point of postoperative atrial fibrillation and the secondary endpoints were major cardiovascular events, death, and readmission due to heart failure.

RESULTS

The surgeries were successfully performed in all subjects of the study. A total of 30 patients were lost to follow-up within 5 years after operation (10.7% losses to follow-up), including 11 patients in the ablation group and 19 patients in the non-ablation group. The proportion of patients who did not have atrial fibrillation in the ablation group and the non-ablation group at 6 months, 12 months, 2 years, 3 years and 5 years after surgery was 83.3% and 27.7%, 72.7% and 20.8%, 66.0% and 15.4% 61.3% and 13.1%, and 43.3% and 10.8%, respectively, with the data from the two groups showing statistically significant difference ( P<0.001). The proportion of patients who did not have atrial fibrillation in the biatrial ablation and the left atrial ablation group at 6 months, 12 months, 2 years, 3 years and 5 years after surgery was 87.3% and 87.5%, 92.4% and 82.8%, 90.5% and 85.7%, 94.8% and 88.1%, and 75.5% and 69.4%%, respectively, with the data from the two groups showing no statistically significant difference ( P>0.05). However, the cumulative incidence of atrial fibrillation in the two groups showed statistically significant difference ( P<0.001). There were no deaths within 30 days after operation in either group. There was no significant difference in cerebrovascular accident, readmission for heart failure, pulmonary infection and mediastinal infection between the two groups within one year after operation ( P>0.05). However, the proportion of patients who had permanent pacemaker installed due to Ⅲ-degree atrioventricular block in the ablation group was higher than that in the non-ablation group ( P<0.05). The proportion of patients who required re-drainage due to delayed pericardial effusion in the ablation group was higher than that in the non-ablation group ( P<0.05).

CONCLUSION

In the group of patients who had modified Cox Maze procedure with bipolar ablation device to treat atrial fibrillation during concomitant mitral valve surgery, the maintenance rate of sinus rhythm after cardioversion was significantly higher than that in the non-ablation group. The surgery showed better safety and significantly better medium- and long-term outcomes. Left atrial ablation and biatrial ablation were both considered safe and effective surgical treatment for atrial fibrillation. Compared with the left atrial ablation group, the biatrial ablation group achieved better effects in restoring and maintaining sinus rhythm without an increase the incidence of perioperative complications.

The characteristics and efficacy of catheter ablation of focal atrial tachycardia arising from an epicardial site

BACKGROUND

Although epicardial structures around the atrium such as adipose tissue possess arrhythmogenicity, little is known about atrial tachycardias (ATs) originating from epicardial sites (Epi-ATs). This study aimed to elucidate the prevalence, characteristics, and outcome after radiofrequency catheter ablation (RFCA) of Epi-ATs and to reveal the association between Epi-ATs and the epicardial structures.

METHODS

The electrocardiographic, electrophysiologic, and anatomical properties and results of RFCA were analyzed in 42 patients with a total of 49 ectopic ATs.

RESULTS

Six Epi-ATs (12%) were observed in six patients (14%). Four of six were respiratory cycle-dependent ATs and one was a swallowing-induced AT. The Epi-AT origins were adjacent to a pulmonary vein (five cases) and vein of Marshall (one case). A Valsalva maneuver or atropine infusion to define the arrhythmia mechanism affected the appearance of the Epi-ATs. The congruity rate between epicardial adipose tissue and the AT origin was significantly higher (100% vs. 44%, p = .045), and the epicardial adipose tissue volume of the atrium was significantly larger (104.1 vs. 64.6 ml, p = .04) in the Epi-AT group. Endocardial RFCA targeting the AT foci resulted in acute success in five of five cases. However, electrical isolation including of the AT foci resulted in acute failures (two of three cases) or a recurrence (one of one case).

CONCLUSIONS

Six Epi-ATs were associated with thoracic veins and epicardial arrhythmogenic structures. The main cause provoking the Epi-ATs was associated with autonomic nerve activity.

Cardiac computational modelling

Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models.

Electrophysiological significance of the interatrial conduction including cavo-tricuspid isthmus during atrial fibrillation

KEY POINTS

The interatrial conduction, including Bachmann's bundle, the posterior septal conduction, the anterior septal conduction, and the cavo-tricuspid isthmus, contributes to the maintenance mechanisms of atrial fibrillation in a 3D biatrial model. The interatrial conduction ablation including a cavo-tricuspid isthmus ablation significantly affects the wave dynamics of atrial fibrillation (AF) and facilitates the AF termination or atrial tachycardia conversion of the AF after the circumferential pulmonary vein isolation. Additional cavo-tricuspid isthmus ablation after the circumferential pulmonary vein isolation improves long-term rhythm outcome after clinical AF catheter ablation.

ABSTRACT

Although it is known that atrial fibrillation (AF) is mainly a left atrial (LA) disease, the role of the right atrium (RA) and interatrial conduction (IAC), including the cavo-tricuspid isthmus (CTI), has not been clearly defined. We tested AF wave dynamics with or without IAC in computational modelling and the rhythm outcome of AF catheter ablation (AFCA) including CTI ablation in clinical cohort data. We evaluated the dominant frequency (DF) in 3D biatrial AF simulations integrated with 3D-computed tomograms obtained from 10 patients. The IAC was implemented at Bachmann's bundle, posterior septum and the CTI. After virtual circumferential PV isolation (CPVI), we disconnected IACs one by one, and observed the wave dynamics. We compared the long-term rhythm outcome after CPVI alone and additional CTI ablation in 846 patients with AFCA. LA-DF was higher than RA-DF in AF (P < 0.001). After CPVI, the DF decreased significantly by additional IAC ablation (P = 0.003), especially in the LA (P = 0.016). The amount of DF reduction (P = 0.020) and rates of AF termination (P < 0.001) or AT conversion (P = 0.021) were significantly higher after IAC ablations including CTI than those without. In clinical AFCA, the AF recurrence rate was significantly lower in patients with additional CTI ablation than CPVI alone during 25 ± 20 months' follow-up (hazard ratio 0.60 [0.46-0.79], P < 0.001, Log rank P < 0.001). IAC contributes to the maintenance mechanism of AF, and IAC including CTI ablation affects AF wave dynamics, facilitating AF termination in 3D biatrial modelling. Additional CTI ablation after CPVI improves the long-term rhythm outcome in clinical AFCA, potentially in a paroxysmal type with accompanying atrial flutter, or atrial dimension close to normal.

The predictive value of heart rate variability indices tested in early period after radiofrequency catheter ablation for the recurrence of atrial fibrillation

INTRODUCTION

To explore the relationship between recurrence of atrial fibrillation (AF) and the autonomic nervous activity evaluated by heart rate variability (HRV) indices after radiofrequency catheter ablation (RFCA) in the early period.

METHODS

We enrolled 102 patients with paroxysmal AF and tested the HRV indices by the high-resolution Holter electrocardiogram the next morning after RFCA. The HRV indices were compared between the non-recurrence group (n = 85) and the recurrence group (n = 17).

RESULTS

The HRV indices included standard deviation of normal to normal intervals (SDNN), SDNN index, root-mean square successive differences (RMSSD), the proportion of normal to normal intervals differing by >50 millisecond (ms) (pNN50), high-frequency components (HF), low-frequency components (LF) and very low-frequency components were significantly higher in recurrence group than that in non-recurrence group, while no such difference was found for LF/HF. Based on the median value of the recurrent time (9 months), RMSSD (P = .012), pNN50 (P < .0001) and HF (P = .033) were lower in late recurrence group than that in early recurrence group. The Cox regression analyses indicated that higher values of RMSSD (P = .01), pNN50 (P = .02) and HF (P = .02) were associated with a higher risk of recurrence after adjusted for covariates. The receiver operating characteristic curves showed higher rates of clinical recurrence of AF after RFCA in patients with RMSSD ≥27.5 ms, pNN50 ≥4.5%, and HF ≥178.25 ms² .

CONCLUSIONS

Values of RMSSD, pNN50, and HF tested in the early period after RFCA could independently predict the recurrence of AF.

Long-Term Outcome of Additional Superior Vena Cava to Septal Linear Ablation in Catheter Ablation of Atrial Fibrillation

Background

We previously reported the benefit of linear ablation from the superior vena cava to the right atrial septum (SVC‐L) within a year after circumferential pulmonary vein isolation (CPVI) in patients with paroxysmal atrial fibrillation (AF). We explored the long‐term effects of SVC‐L and its potential related mechanisms.

Methods and Results

Among 2140 consecutive patients with AF ablation, we included 614 patients (73.3% male, aged 57.8±10.7 years, 13.7% with persistent AF) who did not undergo an extra–pulmonary vein left atrial ablation after propensity score matching; of those, 307 had additional SVC‐L and 307 had CPVI alone. We evaluated the heart rate variability and computational modeling study to explore mechanisms. Although the procedure time was longer in the SVC‐L group than the CPVI group (P<0.001), the complication rates did not differ (P=0.560). During 40.5±24.4 months of follow‐up, the rhythm outcome was significantly better in the SVC‐L group than the CPVI group (log rank, P<0.001). At 2‐year follow‐up of heart rate variability, a significantly higher mean heart rate (P=0.018) and a lower ratio of low/high‐frequency components (P=0.011) were found with SVC‐L than CPVI alone. In realistic in silico biatrial modeling, which reflected the electroanatomies of 10 patients, SVC‐L significantly reduced biatrial dominant frequency compared with CPVI alone (P<0.001) and increased AF termination and defragmentation rates (P=0.033).

Conclusions

SVC‐L ablation in addition to CPVI significantly improved the long‐term rhythm outcome over 2 years after AF catheter ablation by mechanisms involving autonomic modulation and AF organization.

Understanding AF Mechanisms Through Computational Modelling and Simulations

AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

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.

The role of personalized atrial modeling in understanding atrial fibrillation mechanisms and improving treatment

Atrial fibrillation is the most common arrhythmia in humans and is associated with high morbidity, mortality and health-related expenses. Computational approaches have been increasingly utilized in atrial electrophysiology. In this review we summarize the recent advancements in atrial fibrillation modeling at the organ scale. Multi-scale atrial models now incorporate high level detail of atrial anatomy, tissue ultrastructure and fibrosis distribution. We provide the state-of-the art methodologies in developing personalized atrial fibrillation models with realistic geometry and tissue properties. We then focus on the use of multi-scale atrial models to gain mechanistic insights in AF. Simulations using atrial models have provided important insight in the mechanisms underlying AF, showing the importance of the atrial fibrotic substrate and altered atrial electrophysiology in initiation and maintenance of AF. Last, we summarize the translational evidence that supports incorporation of computational modeling in clinical practice for development of personalized treatment strategies for patients with AF. In early-stages clinical studies, AF models successfully identify patients where pulmonary vein isolation alone is not adequate for treatment of AF and suggest novel targets for ablation. We conclude with a summary of the future developments envisioned for the field of atrial computational electrophysiology.

Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy

The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.