Time-Averaged Wavefront Analysis Demonstrates Preferential Pathways of Atrial Fibrillation, Predicting Pulmonary Vein Isolation Acute Response

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.

High-density and high coverage composite mapping of repetitive atrial activation patterns

BACKGROUND

Repetitive atrial activation patterns (RAAPs) during atrial fibrillation (AF) may be associated with localized mechanisms that maintain AF. Current electro-anatomical mapping systems are unsuitable for analyzing RAAPs due to the trade-off between spatial coverage and electrode density in clinical catheters. This work proposes a technique to overcome this trade-off by constructing composite maps from spatially overlapping sequential recordings.

METHODS

High-density epicardial contact mapping was performed during open-chest surgery in goats (n=16, left and right atria) with 3 or 22 weeks of sustained AF (249-electrode array, electrode distance 2.4 mm). A dataset mimicking sequential recordings was generated by segmenting the grid into four spatially overlapping regions (each region 6.5 cm², 48±10% overlap) without temporal overlap. RAAPs were detected in each region using recurrence plots of activation times. RAAPs in two different regions were joined in case of RAAP cross-recurrence between overlapping electrodes. We quantified the reconstruction success rate and quality of the composite maps.

RESULTS

Of 1021 RAAPs found in the full mapping array (32±13 per recording), 328 spatiotemporally stable RAAPs were analyzed. 247 composite maps were generated (75% success) with a quality of 0.86±0.21 (Pearson correlation). Success was significantly affected by the RAAP area. Quality was weakly correlated with the number of repetitions of RAAPs (r=0.13, p<0.05) and not affected by the atrial side (left or right) or AF duration (3 or 22 weeks of AF).

CONCLUSIONS

Constructing composite maps by combining spatially overlapping sequential recordings is feasible. Interpretation of these maps can play a central role in ablation planning.

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

Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold.