Finite Element Modeling Study of the Sensitivity of Electromechanical Dyssynchrony to Left Branch Bundle Block Location

Introduction: : At the center of cardiac contraction in mammalian hearts lies a distinctive network of rapidly firing cells known as Purkinje fibers. Located primarily in the subendocardial layer of the left and right ventricles, this network enables coordinated ventricular activation through Purkinje Myocardial Junctions (PMJs) [1]. These junctions allow for rapid depolarization of the surrounding myocardium, generating a physiological activation pattern during each heartbeat [2].
Cardiac abnormalities such as myocardial infarction, cardiomyopathy, hypertension, and aortic valve disease can impair conductivity in the left ventricle [3]. This reduced conductivity may result in complete or partial blockage [3] within branches of the Purkinje fiber network, with an estimated prevalence of approximately 1 percent in the general population and up to 25 percent in patients with dilated cardiomyopathy [4], [5]. These disruptions lead to delayed activation, which is reflected by a prolongation of the QRS complex on the electrocardiogram [6], [7], reduced ejection fraction [8], and ventricular dyssynchrony [9].
Here, we developed a computational model to simulate both healthy and left bundle branch block (LBBB) activation patterns. The model incorporates an anatomically inspired Purkinje network to analyze the effects of conduction abnormalities in the left ventricle. A healthy (baseline) case was developed and validated using measured activation time data. Thereafter, a sensitivity analysis was conducted to evaluate the impact of LBBB location by comparing conduction blocks situated near the HIS bundle with those near the LV apex.

Materials and

Methods: : A Purkinje fiber network was generated on the endocardial surface of an idealized left ventricle (LV) based on a fractal tree–based algorithm [10]. The algorithm was modified to enable the integration of a deterministic generation of the main trunk originating at the HIS bundle, and a stochastic branch generation for the downstream network. Myofiber orientation in the LV wall was prescribed using a rule-based method [11].
In the computational framework implemented using the FE library FEniCs 2019 [15], cardiac mechanics was described using an active stress formulation [12]. A Robin boundary condition was applied at the epicardium. Electrical activation in the Purkinje fiber network and the LV wall was modeled based on the modified formulation of the FitzHugh–Nagumo equations [13]. In the LV wall, electrical conductivity was anisotropic with fastest conduction occurring in the myofiber direction [14].
To investigate mechanical dyssynchrony attributed to LBBB, conduction blocks were introduced in three distinct regions of the Purkinje network: anterior, septal-apical, and posterior region (Fig. 1, bottom right). Both complete block (zero conductivity) and incomplete block (delayed conductivity) were considered by altering a segment of the fiber network upstream of the regions (Fig. 1, highlighted orange). A global complete block case was also simulated by disabling the entire Purkinje network and initiating activation in the myocardium near the HIS bundle. This setup enabled a systematic evaluation of how the location, severity and extent of Purkinje conduction failure influence electrical activation and mechanical dyssynchrony.

Results, Conclusions, and Discussions:: The distribution of Purkinje–myocardial junctions (PMJs) indicated clustering primarily in lateral segments, with increasing densities towards the apex (Fig. 1 center). This spatial distribution agrees with PMJ maps reported by Garcia et al. [1].
Electrical activation time and pattern in the healthy case are consistent with measurements [2]. Specifically, the model shows transmural propagation from endocardium to epicardium, with a total activation time of 75 ms (Fig. 1 left bottom; Fig. 2e). Total activation time of the entire Purkinje network was 22.5 ms, aligning with sinus rhythm measurements (20.1 ± 7 ms) [15].
Anterior block increased myocardial activation time to 115 ms and reduced Purkinje activation time to 16 ms (Fig. 2a, 2e). Apical and posterior blocks had minimal impact on maximum myocardial activation time. For incomplete blocks, myocardial activation times were 110 ms (anterior), 73.5 ms (septal-apical), and 74.5 ms (posterior) (Fig. 2e). In the global complete block case, maximum myocardial activation time was 200.5 ms (Fig. 2a, 2e).
Ejection fraction remained constant across all cases (56%) and was slightly decreased to 55% in the global block compared to the healthy case (Fig. 2e). Slight reductions in peak LV pressure were observed in the anterior (112.9 mmHg) and global block (110.4 mmHg) case (Fig. 2b). Global strain metrics showed only small changes (Fig. 2d). Displacement field during a cardiac cycle, however, shows asynchronous contraction of the LV, particularly in the anterior and global block scenarios (Fig. 2c). Additionally, pressure and deformation traces suggest a delayed onset of contraction and prolonged relaxation. These observations are consistent with early-stage LBBB, where EF is often preserved [16]. Over time, however, conduction velocity alterations and regional dyssynchrony may contribute to adverse remodeling and eventual EF decline [8]. Capturing such chronic progression would require incorporating time-dependent changes in myocardial properties, such as regional conduction decrease or fibrosis.
In conclusion, both location and degree of Purkinje conduction abnormalities significantly influence activation timing and local contraction dynamics, without substantial changes to global mechanics (Fig. 2a-e). The model provides a foundation for exploring dyssynchrony and will be extended to simulate cardiac resynchronization therapy pacing strategies [18].

Acknowledgements and/or References (Optional):: [1] V. Garcia-Bustos, R. Sebastian, M. Izquierdo, P. Molina, F. J. Chorro, and A. Ruiz-Sauri, “A quantitative structural and morphometric analysis of the Purkinje network and the Purkinje–myocardial junctions in pig hearts,” J Anat, vol. 230, no. 5, 2017, doi: 10.1111/joa.12594.
[2] D. Durrer, R. T. van Dam, G. E. Freud, M. J. Janse, F. L. Meijler, and R. C. Arzbaecher, “Total excitation of the isolated human heart.,” Circulation, vol. 41, no. 6, 1970, doi: 10.1161/01.CIR.41.6.899.
[3] B. Surawicz, R. Childers, B. J. Deal, and L. S. Gettes, “AHA/ACCF/HRS Recommendations for the Standardization and Interpretation of the Electrocardiogram. Part III: Intraventricular Conduction Disturbances A Scientific Statement From the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society,” 2009. doi: 10.1016/j.jacc.2008.12.013.
[4] B. E. Bussink, A. G. Holst, L. Jespersen, J. W. Deckers, G. B. Jensen, and E. Prescott, “Right bundle branch block: Prevalence, risk factors, and outcome in the general population: Results from the Copenhagen City Heart Study,” Eur Heart J, vol. 34, no. 2, 2013, doi: 10.1093/eurheartj/ehs291.
[5] S. Baldasseroni et al., “Left bundle-branch block is associated with increased 1-year sudden and total mortality rate in 5517 outpatients with congestive heart failure: A report from the Italian network on congestive heart failure,” Am Heart J, vol. 143, no. 3, pp. 398–405, Mar. 2002, doi: 10.1067/MHJ.2002.121264.
[6] D. D. Chan et al., “Comparison of the Relation Between Left Ventricular Anatomy and QRS Duration in Patients With Cardiomyopathy With Versus Without Left Bundle Branch Block,” Am J Cardiol, vol. 113, no. 10, pp. 1717–1722, May 2014, doi: 10.1016/J.AMJCARD.2014.02.026.
[7] A. Mirolo et al., “Left bundle branch area pacing in patients with baseline narrow, left, or right bundle branch block QRS patterns: insights into electrocardiographic and echocardiographic features,” Europace, vol. 25, no. 2, 2023, doi: 10.1093/europace/euac223.
[8] N. Y. Tan, C. M. Witt, J. K. Oh, and Y. M. Cha, “Left Bundle Branch Block: Current and Future Perspectives,” 2020. doi: 10.1161/CIRCEP.119.008239.
[9] J. Aalen et al., “Afterload Hypersensitivity in Patients With Left Bundle Branch Block,” JACC Cardiovasc Imaging, vol. 12, no. 6, pp. 967–977, Jun. 2019, doi: 10.1016/J.JCMG.2017.11.025.
[10] F. Sahli Costabal, D. E. Hurtado, and E. Kuhl, “Generating Purkinje networks in the human heart,” J Biomech, vol. 49, no. 12, 2016, doi: 10.1016/j.jbiomech.2015.12.025.
[11] J. D. Bayer, R. C. Blake, G. Plank, and N. A. Trayanova, “A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models,” Ann Biomed Eng, vol. 40, no. 10, 2012, doi: 10.1007/s10439-012-0593-5.
[12] J. M. Guccione, A. D. McCulloch, and L. K. Waldman, “Passive Material Properties of Intact Ventricular Myocardium Determined From a Cylindrical Model,” J Biomech Eng, vol. 113, no. 1, pp. 42–55, Feb. 1991, doi: 10.1115/1.2894084.
[13] R. R. Aliev and A. V. Panfilov, “A Simple Two-variable Model of Cardiac Excitation,” Chaos Solitons Fractals, vol. 7, no. 3, 1996, doi: 10.1016/0960-0779(95)00089-5.
[14] M. S. Alnaes et al., “The FEniCS Project Version 1.5,” Archive of Numerical Software, vol. 3, no. 100, 2015.
[15] D. J. Dosdall, J. Osorio, R. P. Robichaux, J. Huang, L. Li, and R. E. Ideker, “Purkinje activation precedes myocardial activation following defibrillation after long-duration ventricular fibrillation,” Heart Rhythm, vol. 7, no. 3, 2010, doi: 10.1016/j.hrthm.2009.11.025.
[16] E. W. Lau, H. Bonnemeier, and B. Baldauf, “Left bundle branch block—Innocent bystander, silent menace, or both,” Heart Rhythm, Dec. 2024, doi: 10.1016/j.hrthm.2024.12.038.
[17] C. Daubert, N. Behar, R. P. Martins, P. Mabo, and C. Leclercq, “Avoiding non-responders to cardiac resynchronization therapy: A practical guide,” 2017. doi: 10.1093/eurheartj/ehw270.
[18] G. Domenichini et al., “Conduction System Pacing versus Conventional Biventricular Pacing for Cardiac Resynchronization Therapy: Where Are We Heading?,” 2023. doi: 10.3390/jcm12196288.