OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research

Quantification of atrial cardiomyopathy disease severity by electroanatomic voltage mapping and cardiac magnetic resonance imaging

INTRODUCTION

Atrial late gadolinium enhancement (Atrial-LGE) and electroanatomic voltage mapping (Atrial-EAVM) quantify the anatomical and functional extent of atrial cardiomyopathy. We aimed to explore the relationships between, and outcomes from, these modalities in patients with atrial fibrillation undergoing ablation.

METHODS

Patients undergoing first-time ablation had disease severities quantified using both Atrial-LGE and Atrial-EAVM. Correlations between modalities and their relationships with clinical features and arrhythmia recurrence were assessed.

RESULTS

In 123 atrial fibrillation patients (60 ± 10 years), Atrial-EAVM was moderately correlated with Atrial-LGE (r = .34, p < .001), with a mean fibrosis burden of 47.2% ± 14.91%. Agreement was strongest in the highest tertile of fibrosis burden (mean of differences 16.8% (95% CI = -24.4% to 57.9%, p = .433). Fibrosis burden was greater for Atrial-LGE than Atrial-EAVM (50.7% ± 10.7% vs. 13.7% ± 7.13%, p < .005) for patients in the lowest tertile who were younger, had smaller atria and a greater frequency of paroxysmal atrial fibrillation. Both Atrial EAVM and Atrial LGE were associated with recurrence of arrhythmia following ablation (Atrial-LGE HR = 1.02 (95% CI = 1.01-1.04), p = .047; Atrial-EAVM HR = 1.02 (95% CI = 1.005-1.03), p = .007). A low fibrosis burden (<15%) by Atrial-EAVM identified patients with very low arrhythmia recurrence. In contrast, a much higher fibrosis burden (>66%) by Atrial-LGE identified patients failing to respond to ablation.

CONCLUSIONS

We demonstrate for the first time that the level of agreement between Atrial-EAVM and Atrial-LGE is dependent on the level of atrial cardiomyopathy disease severity. The functional consequences of atrial cardiomyopathy are most evident in patients with the highest anatomical extent of disease.

The impact of atrial voltage and conduction velocity phenotypes on atrial fibrillation recurrence

Introduction

Low atrial voltage and slow conduction velocity (CV) have been associated with atrial fibrillation (AF); however, their interaction and relative importance as early disease markers remain incompletely understood. We aimed to elucidate the relationship between atrial voltage and CV using high-density electroanatomic (HDE) maps of patients with AF.

Methods

HDE maps obtained during sinus rhythm in 52 patients with AF and five healthy controls were analysed. Atrial voltage and CV maps were generated, and their correlations were assessed. Subgroup analyses were performed based on clinically relevant factors such as AF type, CV, and voltage levels. Finally, cluster analysis was conducted to identify distinct phenotypes within the population, reflecting different patterns of conduction and voltage.

Results

A moderate positive correlation was found between the mean atrial voltage and CV (r = 0.570). Subgroup analysis revealed differences in voltage (p = 0.0044) but not in global CV (p = 0.42), with no significant differences between AF types. Three distinct phenotypes emerged: normal voltage/normal CV, normal voltage/low CV, and low voltage/low CV, with distinct recurrence rates, suggesting different disease progression paths. Slower atrial CV was identified as a significant predictor of arrhythmia recurrence at 12 and 24 months after AF ablation, surpassing the predictive potential of atrial voltage.

Conclusion

Atrial voltage and CV analyses revealed distinct phenotypes. Lower atrial CV emerged as a significant predictor of AF recurrence, exceeding the predictive significance of atrial voltage. These findings emphasise the importance of considering CV and voltage in managing AF and offer potential insights for personalised strategies.

A Novel Workflow for Electrophysiology Studies in Patients with Brugada Syndrome

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the 'type 1' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.

Endocardial repolarization dispersion in BrS: A novel automatic algorithm for mapping activation recovery interval

INTRODUCTION

Repolarization dispersion in the right ventricular outflow tract (RVOT) contributes to the type-1 electrocardiographic (ECG) phenotype of Brugada syndrome (BrS), while data on the significance and feasibility of mapping repolarization dispersion in BrS patients are scarce. Moreover, the role of endocardial repolarization dispersion in BrS is poorly investigated. We aimed to assess endocardial repolarization patterns through an automated calculation of activation recovery interval (ARI) estimated on unipolar electrograms (UEGs) in spontaneous type-1 BrS patients and controls; we also investigated the relation between ARI and right ventricle activation time (RVAT), and T-wave peak-to-end interval (Tpe) in BrS patients.

METHODS

Patients underwent endocardial high-density electroanatomical mapping (HDEAM); BrS showing an overt type-1 ECG were defined as OType1, while those without (latent type-1 ECG and LType1) received ajmaline infusion. BrS patients only underwent programmed ventricular stimulation (PVS). Data were elaborated to obtain ARI corrected with the Bazett formula (ARIc), while RVAT was derived from activation maps.

RESULTS

39 BrS subjects (24 OType1 and 15 LTtype1) and 4 controls were enrolled. OType1 and post-ajmaline LType1 showed longer mean ARIc than controls (306 ± 27.3 ms and 333.3 ± 16.3 ms vs. 281.7 ± 10.3 ms, p = .05 and p < .001, respectively). Ajmaline induced a significant prolongation of ARIc compared to pre-ajmaline LTtype1 (333.3 ± 16.3 vs. 303.4 ± 20.7 ms, p < .001) and OType1 (306 ± 27.3 ms, p < .001). In patients with type-1 ECG (OTtype1 and post-ajmaline LType1) ARIc correlated with RVAT (r = .34, p = .04) and Tpec (r = .60, p < .001), especially in OType1 subjects (r = .55, p = .008 and r = .65 p < .001, respectively).

CONCLUSION

ARIc mapping demonstrates increased endocardial repolarization dispersion in RVOT in BrS. Endocardial ARIc positively correlates with RVAT and Tpec, especially in OType1.

Conduction velocity is reduced in the posterior wall of hypertrophic cardiomyopathy patients with normal bipolar voltage undergoing ablation for paroxysmal atrial fibrillation

OBJECTIVES

We investigated characteristics of left atrial conduction in patients with HCM, paroxysmal AF and normal bipolar voltage.

BACKGROUND

Patients with hypertrophic cardiomyopathy (HCM) exhibit abnormal cardiac tissue arrangement. The incidence of atrial fibrillation (AF) is increased fourfold in patients with HCM and confers a fourfold increased risk of death. Catheter ablation is less effective in HCM, with twofold increased risk of AF recurrence. The mechanisms of AF perpetuation in HCM are poorly understood.

METHODS

We analyzed 20 patients with HCM and 20 controls presenting for radiofrequency ablation of paroxysmal AF normal left atrial voltage(> 0.5 mV). Intracardiac electrograms were extracted from the CARTO mapping system and analyzed using Matlab/Python code interfacing with Core OpenEP software. Conduction velocity maps were calculated using local activation time gradients.

RESULTS

There were no differences in baseline demographics, atrial size, or valvular disease between HCM and control patients. Patients with HCM had significantly reduced atrial conduction velocity compared to controls (0.44 ± 0.17 vs 0.56 ± 0.10 m/s, p = 0.01), despite no significant differences in bipolar voltage amplitude (1.23 ± 0.38 vs 1.20 ± 0.41 mV, p = 0.76). There was a statistically significant reduction in conduction velocity in the posterior left atrium in HCM patients relative to controls (0.43 ± 0.18 vs 0.58 ± 0.10 m/s, p = 0.003), but not in the anterior left atrium (0.46 ± 0.17 vs 0.55 ± 0.10 m/s, p = 0.05). There was a significant association between conduction velocity and interventricular septal thickness (slope = -0.013, R2 = 0.13, p = 0.03).

CONCLUSIONS

Atrial conduction velocity is significantly reduced in patients with HCM and paroxysmal AF, possibly contributing to arrhythmia persistence after catheter ablation.

AugmentA: Patient-specific augmented atrial model generation tool

Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.

Frequency Domain Analysis of Endocardial Electrograms for Detection of Nontransmural Myocardial Fibrosis in Nonischemic Cardiomyopathy

BACKGROUND

Voltage mapping in nonischemic cardiomyopathy can fail to identify midmyocardial substrate for ventricular arrhythmias, an important cause of ablation failure.

OBJECTIVES

The aim of this study was to assess whether frequency domain analysis of endocardial left ventricular electrograms (EGMs) can better predict the presence of midmyocardial fibrosis (MMF) compared with voltage amplitude.

METHODS

Nonischemic cardiomyopathy patients undergoing ventricular tachycardia ablation with registered preprocedural cardiac computed tomography and late iodine enhancement were included. Presence of fibrosis at each EGM site was assessed. Bipolar and unipolar EGMs were transformed to the frequency domain using multitaper spectral analysis. Singular value decomposition of the EGM frequency spectrum was used within a supervised machine learning process to select features to predict the presence of MMF and compare against predictions using voltage amplitude.

RESULTS

Thirteen patients were included (median age 57 years [IQR: 28-73 years], median ejection fraction 40% [IQR: 15%-57%]). A total of 6,015 EGM pairs were processed: 2,459 EGM pairs in MMF areas and 3,556 EGM pairs in non-MMF areas. Supervised classifiers were trained with stratified k-fold cross-validation within patients. The distribution of mean area under the curve metrics using frequency features, f, was significantly greater than voltage feature area under the curve metrics, v, (mean f = 0.841 [95% CI: 0.789-0.884] vs mean v = 0.591 [95% CI: 0.530-0.658]; P < 0.001), indicating that frequency-trained classifiers better predicted the presence of MMF.

CONCLUSIONS

These data indicate the promising discriminatory value of endocardial EGM frequency content in the assessment of concealed myocardial substrate. Further studies are needed to investigate the importance of the specific frequency features identified.

Computer-aided detection of arrhythmogenic sites in post-ischemic ventricular tachycardia

Nowadays, catheter-based ablation in patients with post-ischemic ventricular tachycardia (VT) is performed in arrhythmogenic sites identified by electrophysiologists by visual inspection during electroanatomic mapping. This work aims to present the development of machine learning tools aiming at supporting clinicians in the identification of arrhythmogenic sites by exploiting innovative features that belong to different domains. This study included 1584 bipolar electrograms from nine patients affected by post-ischemic VT. Different features were extracted in the time, time scale, frequency, and spatial domains and used to train different supervised classifiers. Classification results showed high performance, revealing robustness across the different classifiers in terms of accuracy, true positive, and false positive rates. The combination of multi-domain features with the ensemble tree is the most effective solution, exhibiting accuracies above 93% in the 10-time 10-fold cross-validation and 84% in the leave-one-subject-out validation. Results confirmed the effectiveness of the proposed features and their potential use in a computer-aided system for the detection of arrhythmogenic sites. This work demonstrates for the first time the usefulness of supervised machine learning for the detection of arrhythmogenic sites in post-ischemic VT patients, thus enabling the development of computer-aided systems to reduce operator dependence and errors, thereby possibly improving clinical outcomes.

How synergy between mechanistic and statistical models is impacting research in atrial fibrillation

Atrial fibrillation (AF) with multiple complications, high morbidity and mortality, and low cure rates, has become a global public health problem. Although significant progress has been made in the treatment methods represented by anti-AF drugs and radiofrequency ablation, the therapeutic effect is not as good as expected. The reason is mainly because of our lack of understanding of AF mechanisms. This field has benefited from mechanistic and (or) statistical methodologies. Recent renewed interest in digital twin techniques by synergizing between mechanistic and statistical models has opened new frontiers in AF analysis. In the review, we briefly present findings that gave rise to the AF pathophysiology and current therapeutic modalities. We then summarize the achievements of digital twin technologies in three aspects: understanding AF mechanisms, screening anti-AF drugs and optimizing ablation strategies. Finally, we discuss the challenges that hinder the clinical application of the digital twin heart. With the rapid progress in data reuse and sharing, we expect their application to realize the transition from AF description to response prediction.

Effect of scar and pacing location on repolarization in a porcine myocardial infarction model

Background

The effect of chronic ischemic scar on repolarization is unclear, with conflicting results from human and animal studies. An improved understanding of electrical remodeling within scar and border zone tissue may enhance substrate-guided ablation techniques for treatment of ventricular tachycardia. Computational modeling studies have suggested increased dispersion of repolarization during epicardial, but not endocardial, left ventricular pacing, in close proximity to scar. However, the effect of endocardial pacing near scar in vivo is unknown.

Objective

The purpose of this study was to investigate the effect of scar and pacing location on local repolarization in a porcine myocardial infarction model.

Methods

Six model pigs underwent late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging followed by electroanatomic mapping of the left ventricular endocardium. LGE-CMR images were registered to the anatomic shell and scar defined by LGE. Activation recovery intervals (ARIs), a surrogate for action potential duration, and local ARI gradients were calculated from unipolar electrograms within areas of late gadolinium enhancement (aLGE) and healthy myocardium.

Results

There was no significant difference between aLGE and healthy myocardium in mean ARI (304.20 ± 19.44 ms vs 300.59 ± 19.22 ms; P = .43), ARI heterogeneity (23.32 ± 11.43 ms vs 24.85 ± 12.99 ms; P = .54), or ARI gradients (6.18 ± 2.09 vs 5.66 ± 2.32 ms/mm; P = .39). Endocardial pacing distance from scar did not affect ARI gradients.

Conclusion

Our findings suggest that changes in ARI are not an intrinsic property of surviving myocytes within scar, and endocardial pacing close to scar does not affect local repolarization.

Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction

BACKGROUND

Computational models of the heart built from cardiac MRI and electrophysiology (EP) data have shown promise for predicting the risk of and ablation targets for myocardial infarction (MI) related ventricular tachycardia (VT), as well as to predict paced activation sequences in heart failure patients. However, most recent studies have relied on low resolution imaging data and little or no EP personalisation, which may affect the accuracy of model-based predictions.

OBJECTIVE

To investigate the impact of model anatomy, MI scar morphology, and EP personalisation strategies on paced activation sequences and VT inducibility to determine the level of detail required to make accurate model-based predictions.

METHODS

Imaging and EP data were acquired from a cohort of six pigs with experimentally induced MI. Computational models of ventricular anatomy, incorporating MI scar, were constructed including bi-ventricular or left ventricular (LV) only anatomy, and MI scar morphology with varying detail. Tissue conductivities and action potential duration (APD) were fitted to 12-lead ECG data using the QRS duration and the QT interval, respectively, in addition to corresponding literature parameters. Paced activation sequences and VT induction were simulated. Simulated paced activation and VT inducibility were compared between models and against experimental data.

RESULTS

Simulations predict that the level of model anatomical detail has little effect on simulated paced activation, with all model predictions comparing closely with invasive EP measurements. However, detailed scar morphology from high-resolution images, bi-ventricular anatomy, and personalized tissue conductivities are required to predict experimental VT outcome.

CONCLUSION

This study provides clear guidance for model generation based on clinical data. While a representing high level of anatomical and scar detail will require high-resolution image acquisition, EP personalisation based on 12-lead ECG can be readily incorporated into modelling pipelines, as such data is widely available.

A Divergence-Based Approach for the Identification of Atrial Fibrillation Focal Drivers From Multipolar Mapping: A Computational Study

The expanding role of catheter ablation of atrial fibrillation (AF) has stimulated the development of novel mapping strategies to guide the procedure. We introduce a novel approach to characterize wave propagation and identify AF focal drivers from multipolar mapping data. The method reconstructs continuous activation patterns in the mapping area by a radial basis function (RBF) interpolation of multisite activation time series. Velocity vector fields are analytically determined, and the vector field divergence is used as a marker of focal drivers. The method was validated in a tissue patch cellular automaton model and in an anatomically realistic left atrial (LA) model with Courtemanche-Ramirez-Nattel ionic dynamics. Divergence analysis was effective in identifying focal drivers in a complex simulated AF pattern. Localization was reliable even with consistent reduction (47%) in the number of mapping points and in the presence of activation time misdetections (noise <10% of the cycle length). Proof-of-concept application of the method to human AF mapping data showed that divergence analysis consistently detected focal activation in the pulmonary veins and LA appendage area. These results suggest the potential of divergence analysis in combination with multipolar mapping to identify AF critical sites. Further studies on large clinical datasets may help to assess the clinical feasibility and benefit of divergence analysis for the optimization of ablation treatment.