Automatic estimation of Purkinje‐Myocardial junction hot‐spots from noisy endocardial samples: A simulation study

Barber, Fernando; García-Fernández, Ignacio; Lozano, Miguel; Sebastián, Ramón Alonso

doi:10.1002/cnm.2988

Cited by 11 publications

(11 citation statements)

References 36 publications

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“…In that case, the simulation results of this study should be considered with caution. Although new methodologies are arising to better describe the PS (Lee et al, 2014; Barber et al, 2018). The lack of technology to characterize the PS in a patient specific manner, limits the optimal configuration for CRT.…”

Section: Discussionmentioning

confidence: 99%

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

et al. 2019

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Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of pacing leads to improve CRT response. We use a 3D electrophysiological computational model of the heart and torso to get insight into the changes in the activation patterns obtained when the heart is paced from different regions and for different atrioventricular and interventricular delays. The model represents a heart with left bundle branch block and heart failure, and allows a detailed and accurate analysis of the electrical changes observed simultaneously in the myocardium and in the QRS complex computed in the precordial leads. Computational simulations were performed using a modified version of the O'Hara et al. action potential model, the most recent mathematical model developed for human ventricular electrophysiology. The optimal location for the pacing leads was determined by QRS maximal reduction. Additionally, the influence of Purkinje system on CRT response was assessed and correlation analysis between several parameters of the QRS was made. Simulation results showed that the right ventricle (RV) upper septum near the outflow tract is an alternative location to the RV apical lead. Furthermore, LV endocardial pacing provided better results as compared to epicardial stimulation. Finally, the time to reach the 90% of the QRS area was a good predictor of the instant at which 90% of the ventricular tissue was activated. Thus, the time to reach the 90% of the QRS area is suggested as an additional index to assess CRT effectiveness to improve biventricular synchrony.

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Section: Discussionmentioning

confidence: 99%

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

et al. 2019

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“…This enables the investigation of PCs as arrhythmia triggers [61,62], in addition to their contribution to the substrate for reentry circuits and retrograde propagation [63,64]. Therefore, the integration of the Trovato2020 model in tissue or whole-ventricular models, including the Purkinje tree [65,66], could enable investigations into the role of PCs in human ventricular arrhythmias, caused by relevant disease conditions such as myocardial infarction [67][68][69][70][71], ischemic heart disease [72,73], structural heart disease [74], CPVT [75], post-shock arrhythmia [64], Brugada and Long QT syndromes, both acquired or drug-induced [3,76,77].…”

Section: Eads and Dads Mechanismsmentioning

confidence: 99%

Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities

Trovato

Passini

Nagy

et al. 2020

Journal of Molecular and Cellular Cardiology

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Cardiac Purkinje cells (PCs) are implicated in lethal arrhythmias caused by cardiac diseases, mutations, and drug action. However, the pro-arrhythmic mechanisms in PCs are not entirely understood, particularly in humans, as most investigations are conducted in animals. The aims of this study are to present a novel human PCs electrophysiology biophysically-detailed computational model, and to disentangle ionic mechanisms of human Purkinje-related electrophysiology, pacemaker activity and arrhythmogenicity. The new Trovato2020 model incorporates detailed Purkinje-specific ionic currents and Ca 2+ handling, and was developed, calibrated and validated using human experimental data acquired at multiple frequencies, both in control conditions and following drug application. Multiscale investigations were performed in a Purkinje cell, in fibre and using an experimentally-calibrated population of PCs to evaluate biological variability. Simulations demonstrate the human Purkinje Trovato2020 model is the first one to yield: (i) all key AP features consistent with human Purkinje recordings; (ii) Automaticity with funny current up-regulation (iii) EADs at slow pacing and with 85% hERG block; (iv) DADs following fast pacing; (v) conduction velocity of 160 cm/s in a Purkinje fibre, as reported in human. The human in silico PCs population highlights that: (1) EADs are caused by I CaL reactivation in PCs with large inward currents; (2) DADs and triggered APs occur in PCs experiencing Ca 2+ accumulation, at fast pacing, caused by large L-type calcium current and small Na + /Ca 2+ exchanger. The novel human Purkinje model unlocks further investigations into the role of cardiac Purkinje in ventricular arrhythmias through computer modeling and multiscale simulations.

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“…This method closely follows the actual anatomy of the heart [41], and it is indicated in the case of a healthy activation and a (complete or partial) bundle branch block. When dense endocardial mapping data are available, the Purkinje network can be estimated automatically [32,2,40]. However, reconstructing the network from remote (e.g.…”

Section: Related Workmentioning

confidence: 99%

GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

Grandits,

Effland,

Pock

et al. 2021

Preprint

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The personalization of cardiac models is the cornerstone of patient-specific modeling. Ideally, non-invasive or minimally-invasive clinical data, such as the standard ECG or intracardiac contact recordings, could provide an insight on model parameters. Parameter selection of such models is however a challenging and potentially time-consuming task.In this work, we estimate the earliest activation sites governing the cardiac electrical activation. Specifically, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify their locations and times. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective functional, which in the case of GEASI is the quadratic mismatch to given activation measurements. This versatile approach can be extended for computing topological gradients to estimate the number of sites, or fitting a given ECG.We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.

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Automatic estimation of Purkinje‐Myocardial junction hot‐spots from noisy endocardial samples: A simulation study

Cited by 11 publications

References 36 publications

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study

Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities

GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

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