Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data

Abstract

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution).

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

“…However, further improvements of personalised models could be performed in future to assess effects of more specific simulation of the Purkinje network morphology and LBBB level; scar/fibrosis area location, shape and texture; and tailoring personalised electrophysiological parameters based on the morphology of the QRS complex or the entire time-dependent ECG signal. Several approaches to the latter problem, assuming spatial heterogeneity of conductivity within the myocardial tissue and taking into account partially excitable areas of damaged myocardium, have recently been developed by several groups, including our team ( Moreau-Villéger et al, 2006 ; Albors et al, 2022 ), and could be implemented to fit model predictions to personal clinical data.…”