Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate

Performance of atrial conduction velocity algorithms with error-prone clinical measurements for the identification of atrial fibrosis

INTRODUCTION

Measuring conduction velocity, as a direct consequence of fibrosis, may provide a better method to localise fibrotic regions. This study aims to assess established cardiac conduction velocity calculation methods (Triangulation, Polynomial Surface Fitting, and Radial Basis Function) in identifying areas of conduction slowing caused by fibrosis, considering realistic measurement errors.

METHOD

Using a human left atrium computational model, atrial activation was simulated. Each conduction velocity calculation method's performance was evaluated under uncertainties in mapping point density, local activation time assignment and electrode locations by comparing calculated conduction velocity to ground truth conduction velocity derived from high-resolution simulated atrial activation.

RESULTS

All methods agreed well with ground truth conduction velocity maps in noise-free, high-density sampling conditions. However, Triangulation and Polynomial Surface Fitting methods showed susceptibility to noise, exhibiting significant errors under moderate to high noise levels. Radial Basis Function method demonstrated greater robustness to noise and reduced sampling density. Fibrotic region identification accuracy was high under ideal conditions for all methods but declined with increasing noise, with the Radial Basis Function method maintaining superior performance.

CONCLUSION

While all methods accurately estimate conduction velocity under ideal conditions, the Radial Basis Function method shows robustness to a realistic clinical noise, hence making it the most suitable to identify fibrotic regions.

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic versus data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parametrization, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardized and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic and therapeutic approaches.This article is part of the theme issue 'Science into the next millennium: 25 years on'.

The Accuracy of Cardiac Surface Conduction Velocity Measurements

BACKGROUND

Conduction velocity (CV) is a measure of the health of myocardial tissue. It can be measured by taking differences in local activation times from intracardiac electrodes. Several factors introduce error into the measurement, among which ignoring the 3-dimensional aspect is a major detriment.

OBJECTIVES

The purpose of this study was to determine if, nonetheless, there was a specific region where CV could be accurately measured.

METHODS

Computer simulations of 3-dimensional ventricles with a realistic His-Purkinje system were performed. Ventricles also included a dense scar or diffuse fibrosis.

RESULTS

A finer spatial sampling produced better agreement with true CV. Using an error limit of 10 cm/s as a threshold, measurements taken within a region <2 cm from the pacing site proved to be accurate. Error increased abruptly beyond this distance. The Purkinje system and tissue fiber orientation played equally major roles in leading to a surface CV that was not reflective of the CV propagation through the tissue.

CONCLUSIONS

In general, surface CV correlates poorly with tissue CV. Only surface CV measurements close to the pacing site, taken with an electrode spacing of ≤1 mm, give reasonable estimates.

Identifying extra pulmonary vein targets for persistent atrial fibrillation ablation: bridging advanced and conventional mapping techniques

AIMS

Advanced technologies such as charge density mapping (CDM) show promise in guiding adjuvant ablation in patients with persistent atrial fibrillation (AF); however, their limited availability restricts widespread adoption. We sought to determine whether regions of the left atrium containing CDM-identified pivoting and rotational propagation patterns during AF could also be reliably identified using more conventional contact mapping techniques.

METHODS AND RESULTS

Twenty-two patients undergoing de novo ablation of persistent AF underwent both CDM and electroanatomic voltage mapping during AF and sinus rhythm with multiple pacing protocols. Through the use of a left atrium statistical shape model, the location of distinctive propagation patterns identified by CDM was compared with low-voltage areas (LVAs) and regions of slow conduction velocity (CV). Neither LVA nor CV mapping during paced rhythms reliably identified regions containing CDM propagation patterns. Conduction velocity mapping during AF did correlate with these regions (ρ = -0.63, P < 0.0001 for pivoting patterns; ρ = -0.54, P < 0.0001 for rotational patterns). These propagation patterns consistently occurred in two specific anatomical regions across patients: the anteroseptal and inferoposterior walls of the left atrium.

CONCLUSION

Mapping techniques during paced rhythms do not reliably correspond with regions of CDM-identified propagation patterns in persistent AF. However, these propagation patterns are consistently observed in two specific anatomical regions, suggesting a predisposition to abnormal electrophysiological properties. While further research is needed, these regions may serve as promising targets for empirical ablation, potentially reducing the reliance on complex mapping techniques.

3D Spatiotemporal Activation Mapping of Cardiac Organoids Using Conformal Shell Microelectrode Arrays (MEAs)

Cardiac organoids have emerged as transformative models for investigating cardiogenesis and cardiac diseases. While traditional 2D microelectrode arrays (MEAs) have been used to assess the functionality of cardiac organoids, they are limited to electrophysiological measurements from a single plane and do not capture the 3D propagation of electrical signals. Here, we present a programmable, shape-adaptive shell MEA designed to map the electrical activity across the entire surface of cardiac organoids. These shell MEAs are fabricated on-chip, with tunable dimensions and electrode layout, enabling precise encapsulation of spherical organoids. Using shell MEAs, we generated 3D isochrone maps with conduction velocity vectors, revealing the speed and trajectory of electrical signal propagation in spontaneously beating cardiac organoids. The optical transparency of the shell MEAs allowed for simultaneous calcium imaging, validating the electrophysiological propagation pattern. To demonstrate their utility in cardiotoxicity screening, we monitored the electrophysiological changes of organoids treated with isoproterenol and E-4031 over nine days. We anticipate that shell MEAs, combined with spatiotemporal mapping, can significantly advance the development of spatially organized cardiac organoids, structural disease models, and high-throughput drug screening platforms.

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.

Omnipolar conduction velocity mapping for ventricular substrate characterization: Impact of CV estimation method and EGM type on in vivo conduction velocity measurements

BACKGROUND

Areas of abnormal or heterogeneous conduction velocity (CV) are important ablation targets for ventricular tachycardias, yet precise assessment of CV in clinical contact mapping remains challenging. Numerous different CV estimation methods have been proposed.

OBJECTIVE

This study aimed to compare the automated local activation time (LAT)-independent omnipolar-based CV estimation method termed wave speed (WS) with 4 established LAT-based methods to formally establish the quantitative differences between them.

METHODS

High-density contact maps in patients with structurally normal hearts during sinus rhythm (SR) and ventricular ectopy (VE) were retrospectively analyzed. CV was assessed and compared by 5 methods: omnipolar WS, gradient method, planar wavefront fitting, circular wavefront fitting, and radial basis function. CV variations based on electrogram (EGM) type (unipolar, bipolar, and omnipolar), catheter movement, and surrogate markers for catheter contact were analyzed.

RESULTS

The study included 23 patients (47.8% male; 45.7 ± 17.3 years) with 22 SR maps (11 left ventricle, 11 right ventricle) and 16 VE maps (9 left ventricle, 7 right ventricle). The WS algorithm yielded statistically significant higher CV estimates in SR (mean, 1.41 ± 0.18 m/s) and VE (mean, 1.23 ± 0.18 m/s) maps compared with all LAT-based estimation methods, with absolute differences ranging from 0.1 m/s to 0.81 m/s. Median pointwise differences in SR and VE between WS and LAT-based methods were high, ranging from 0.55 ± 0.15 m/s (WS vs planar wavefront fitting) to 0.67 ± 0.16 m/s (WS vs radial basis function). For LAT-based methods, use of unipolar EGMs yielded significantly higher CV estimates than bipolar or omnipolar EGMs in SR.

CONCLUSION

The CV estimation method has an important, statistically significant impact on ventricular CV measurements. Future work will focus on how these differences affect identification of pathologic conduction slowing in scar-related substrate.

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.

Sequential Intracardiac Activation Time Mapping of Arrhythmias Without Fiducial Time References

Sequential local activation time (LAT) mapping of intracardiac electrograms' activations requires a stable reference signal to align recording phases.

OBJECTIVE

This work's purpose is to develop an LAT mapping approach that does not rely on a time-alignment reference (TAR).

METHODS

To create an LAT map in absence of TAR (TARLess), the coordinates and LATs of recording electrodes are collected sequentially; a bank of candidate functions (CFs) is constructed that contains constant binary level CFs and non-linear functions of recording points' coordinates. Finally, a subset of CFs is linearly combined to create an activation time function with output matching electrodes' LATs. Synthetic and clinical data were deployed to validate TARLess. A simple two-dimensional computer model was used to create 30 different wavefront collision scenarios in a region with spatial conduction heterogeneities. Furthermore, sequential recordings were collected from seven atrial fibrillation patients during stimulation from one or two sites, after sinus rhythm was achieved post catheter ablation.

RESULTS

We showed that TARLess maps are similar to the one that uses TAR; for the 20 clinical maps, the mean absolute difference between measured LAT with the TAR and TARLess approach was 5.2 ±2.0 milliseconds.

CONCLUSION

We developed a novel method to create an LAT map of sequential recordings without using any TAR and showed that it can create accurate maps even during the collision of multiple wavefronts.

SIGNIFICANCE

TARLess mapping does not require a reference catheter which could lead to reduction in ablation procedure duration, cost, and potential complications.

The role of computational methods in cardiovascular medicine: a narrative review

Background and Objective

Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data with physical knowledge of the processes underlying the heart function. These models have been increasingly employed in clinical practice to understand pathological mechanisms and their progression, design medical devices, support clinicians in improving therapies. Hinging upon a long-year experience in cardiovascular modeling, we have recently constructed a computational multi-physics and multi-scale integrated model of the heart for the investigation of its physiological function, the analysis of pathological conditions, and to support clinicians in both diagnosis and treatment planning. This narrative review aims to systematically discuss the role that such model had in addressing specific clinical questions, and how further impact of computational models on clinical practice are envisaged.

Methods

We developed computational models of the physical processes encompassed by the heart function (electrophysiology, electrical activation, force generation, mechanics, blood flow dynamics, valve dynamics, myocardial perfusion) and of their inherently strong coupling. To solve the equations of such models, we devised advanced numerical methods, implemented in a flexible and highly efficient software library. We also developed computational procedures for clinical data post-processing-like the reconstruction of the heart geometry and motion from diagnostic images-and for their integration into computational models.

Key Content and Findings

Our integrated computational model of the heart function provides non-invasive measures of indicators characterizing the heart function and dysfunctions, and sheds light on its underlying processes and their coupling. Moreover, thanks to the close collaboration with several clinical partners, we addressed specific clinical questions on pathological conditions, such as arrhythmias, ventricular dyssynchrony, hypertrophic cardiomyopathy, degeneration of prosthetic valves, and the way coronavirus disease 2019 (COVID-19) infection may affect the cardiac function. In multiple cases, we were also able to provide quantitative indications for treatment.

Conclusions

Computational models provide a quantitative and detailed tool to support clinicians in patient care, which can enhance the assessment of cardiac diseases, the prediction of the development of pathological conditions, and the planning of treatments and follow-up tests.

Conduction velocity mapping in atrial fibrillation using omnipolar technology

BACKGROUND

Recent studies have shown that atrial slow conduction velocity (CV) is associated with the perpetuation of atrial fibrillation (AF). However, the criteria of CV measurement have not been standardized. The aim of this study was to evaluate the relationship between the slow CV area (SCVA) measured by novel omnipolar technology (OT) and AF recurrence.

METHODS

This study included 90 patients with AF who underwent initial pulmonary vein isolation (PVI). The segmented surface area of the SCVA was measured by left atrial (LA) electrophysiological mapping using OT before the PVI. The proportion of the SCVA at each cutoff value of CV (from < 0.6 to < 0.9 m/s) was compared between the patients with and without AF recurrence.

RESULTS

During a mean follow-up period of 516 ± 197 days, the recurrence of AF after the initial PVI was observed in 23 (25.5%) patients. In patients with AF recurrence, the proportion of the SCVA in the LA posterior, LA appendage (LAA), and LA anterior were significantly higher than those without AF recurrence. The multivariate analysis indicated that the proportion of the low voltage area and the SCVA in the LA anterior (local CV < 0.7 m/s) were independent predictors of AF recurrence (hazard ratio [HR], 1.07; 95% confidence interval [CI], 1.01-1.14; p = 0.03; HR, 1.40; 95% CI, 1.07-1.83; p = 0.01, respectively).

CONCLUSION

By evaluating the local CV using OT, it was indicated that SCVA with CV < 0.7 m/s in the LA anterior is strongly associated with AF recurrence after PVI.

[New mapping tools for catheter ablation of atrial fibrillation]

The pulmonary veins have been recognized as the primary source of atrial triggers, and their isolation has become the cornerstone for ablation of atrial fibrillation. However, long-term success rates after pulmonary vein isolation (PVI) are limited. Several promising new mapping techniques are described in this article, aiming to better understand the mechanisms underlying the induction and maintenance of atrial fibrillation and to develop more effective ablation strategies.