Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps

Barber, Fernando; Langfield, Peter; Lozano, Miguel; García-Fernández, Ignacio; Duchâteau, Josselin; Hocini, Mélèze; Haı̈ssaguerre, Michel; Vigmond, Edward J.; Sebastián, Ramón Alonso

doi:10.1109/tmi.2021.3073499

Cited by 18 publications

(8 citation statements)

References 37 publications

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“…Potential causes for the PK retrograde activation might be the more transmural PK system in pigs compared to humans, which lead to incomplete LBBB such as in Pig 3 (anterior PK branch being functional while posterior branch being damaged, affecting the epicardial propagation). Modeling solutions with dedicated PK models such as in [29,39] could explain their better performance in these cases, justifying the use of more detailed and personalized PK system estimation algorithms [46,48].…”

Section: Benchmark Analysis Of Meshless and Finite-element Methods So...mentioning

confidence: 99%

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

et al. 2022

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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).

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Section: Benchmark Analysis Of Meshless and Finite-element Methods So...mentioning

confidence: 99%

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

et al. 2022

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“…The BrS region is made by BrS myocytes and has a homogeneous percentage of diffuse fibrosis. Before starting the simulations, the tissue was initialized in the resting state (i.e., V M = −85 mV , u = 0, w = 0), then the 3D slab was stimulated from the endocardial layer with a square wave moving at 200 cm s [10] to mimic the activation from the Purkinje Network. In healthy conditions, the total activation time of the 3D slab was ≃ 50ms, which is reasonable for the human RV [10].…”

Section: Methodsmentioning

confidence: 99%

“…Before starting the simulations, the tissue was initialized in the resting state (i.e., V M = −85 mV , u = 0, w = 0), then the 3D slab was stimulated from the endocardial layer with a square wave moving at 200 cm s [10] to mimic the activation from the Purkinje Network. In healthy conditions, the total activation time of the 3D slab was ≃ 50ms, which is reasonable for the human RV [10]. To stimulate the cardiac tissue, we used strength twice the diastolic threshold for 2 ms. We performed temporal integration using an explicit Euler scheme (time step of ∆t = 0.02 ms) and we approximated spatial derivatives with standard second-order finite differences (spatial resolution of ∆x = 0.015 cm).…”

Section: Methodsmentioning

confidence: 99%

A Computational Model of Brugada Syndrome in 3D Heterogeneous Cardiac Tissue

Seghetti¹,

Biasi²,

Laurino³

et al. 2022

Computing in Cardiology Conference (CinC)

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Brugada syndrome is a genetic cardiac disorder associated with ventricular arrhythmias and sudden cardiac death. There are two classical interpretations of the ECG features and pathophysiological mechanism of the BrS: the repolarization disorder theory and the depolarization disorder theory. We employed our previously published phenomenological model of human myocytes to simulate the electrical activity of cardiac tissue in a 3D transmurally heterogeneous slab. Furthermore, we modified the model to reproduce the characteristics commonly associated to BrS action potentials. We assessed the insurgence of sustained reentry as a function of electrophysiological alterations and fibrosis distribution. Additionally, for each simulation, we computed simulated epicardial unipolar electrograms. Our results suggest that both electrophysiological and structural alterations are important factors in the induction of sustained reentry associated to BrS.

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“…To simulate LBBB activation patterns in our models, we used a right branch of a synthetic model of the Purkinje network ( Ijiri et al, 2008 ; Sebastian et al, 2011 ; Barber et al, 2021 ) with the left branch completely excluded. Previously, such Purkinje network models have been shown to reproduce an anatomical structure and myocardial activation patterns in the human and rabbit heart in norm and LBBB ( Barber et al, 2021 ; Zhu et al, 2021 ). At the same time, there are experimental data on the morphological varieties of the Purkinje fibre network in mammalian hearts ( Ono et al, 2009 ).…”

Section: Strengths and Limitations Of The Studymentioning

confidence: 99%

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

et al. 2023

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Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude.Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient.Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%).Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.

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Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps

Cited by 18 publications

References 37 publications

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

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

A Computational Model of Brugada Syndrome in 3D Heterogeneous Cardiac Tissue

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

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