Protective Role of False Tendon in Subjects with Left Bundle Branch Block: A Virtual Population Study

Abstract

False tendons (FTs) are fibrous or fibromuscular bands that can be found in both the normal and abnormal human heart in various anatomical forms depending on their attachment points, tissue types, and geometrical properties. While FTs are widely considered to affect the function of the heart, their specific roles remain largely unclear and unexplored. In this paper, we present an in silico study of the ventricular activation time of the human heart in the presence of FTs. This study presents the first computational model of the human heart that includes a FT, Purkinje network, and papillary muscles. Based on this model, we perform simulations to investigate the effect of different types of FTs on hearts with the electrical conduction abnormality of a left bundle branch block (LBBB). We employ a virtual population of 70 human hearts derived from a statistical atlas, and run a total of 560 simulations to assess ventricular activation time with different FT configurations. The obtained results indicate that, in the presence of a LBBB, the FT reduces the total activation time that is abnormally augmented due to a branch block, to such an extent that surgical implant of cardiac resynchronisation devices might not be recommended by international guidelines. Specifically, the simulation results show that FTs reduce the QRS duration at least 10 ms in 80% of hearts, and up to 45 ms for FTs connecting to the ventricular free wall, suggesting a significant reduction of cardiovascular mortality risk. In further simulation studies we show the reduction in the QRS duration is more sensitive to the shape of the heart then the size of the heart or the exact location of the FT. Finally, the model suggests that FTs may contribute to reducing the activation time difference between the left and right ventricles from 12 ms to 4 ms. We conclude that FTs may provide an alternative conduction pathway that compensates for the propagation delay caused by the LBBB. Further investigation is needed to quantify the clinical impact of FTs on cardiovascular mortality risk.

Predicting Plausible Human Purkinje Network Morphology from Simulations

“…To obtain the final surface mesh the mean mesh is deformed based on the SSM using radial basis functions [6]. On the surface mesh of the heart, a PN is grown using [7] with added control over the coverage of the endocardium by the PN. In detail, there is each one Purkinje parameter for the left and right BB bifurcation point, and two controlling the free basal area in the left ventricle (LV) and right ventricle (RV) (Fig 1).…”

Improved hybrid/GPU algorithm for solving cardiac electrophysiology problems on Purkinje networks

“…In the past, different models for the PN have been suggested and reviewed by Ten Tusscher and Panfilov in 2008 . More recent work can be found in the literature . In such simulations, 2 main types of models have been used: the simpler eikonal model and the monodomain model.…”

“…The leads to experiments with a variety of different configurations in shape, size, and possibly dysfunction of the hear. The variation in parameters leads to large studies, as our previous study with more than 700 simulations. We call these type of studies, where all experiments are performed by a computer simulation, in silico studies.…”

“…However, in recent years in silico experiments have become feasible and can provide information about the PN, which are difficult to obtain from clinical settings. 6,7 To obtain the PN, fractal laws based on an a priori anatomical knowledge have been proposed in Abboud et al 8 and Sebastian et al, 9 whereas techniques to generate personalized PN obtained starting from measures of the ventricular activation times have been reported in Vergara et al 10 and Palamara et al 11 It has even been suggested to include the PN in cardiac simulations designed to aid interventions. 6 In the past, different models for the PN have been suggested and reviewed by Ten Tusscher and Panfilov in 2008.…”

“…12 More recent work can be found in the literature. 7,[13][14][15][16] In such simulations, 2 main types of models have been used: the simpler eikonal model and the monodomain model.…”