Literature DB >> 14760923

Timing of depolarization and contraction in the paced canine left ventricle: model and experiment.

Roy C P Kerckhoffs1, Owen P Faris, Peter H M Bovendeerd, Frits W Prinzen, Karel Smits, Elliot R McVeigh, Theo Arts.   

Abstract

INTRODUCTION: For efficient pump function, contraction of the heart should be as synchronous as possible. Ventricular pacing induces asynchrony of depolarization and contraction. The degree of asynchrony depends on the position of the pacing electrode. The aim of this study was to extend an existing numerical model of electromechanics in the left ventricle (LV) to the application of ventricular pacing. With the model, the relation between pacing site and patterns of depolarization and contraction was investigated. METHODS AND
RESULTS: The LV was approximated by a thick-walled ellipsoid with a realistic myofiber orientation. Propagation of the depolarization wave was described by the eikonal-diffusion equation, in which five parameters play a role: myocardial and subendocardial velocity of wave propagation along the myofiber cm and ce; myocardial and subendocardial anisotropy am and ae; and parameter k, describing the influence of wave curvature on wave velocity. Parameters cm, ae, and k were taken from literature. Parameters am and ce were estimated by fitting the model to experimental data, obtained by pacing the canine left ventricular free wall (LVFW). The best fit was found with cm = 0.75 m/s, ce = 1.3 m/s, am = 2.5, ae = 1.5, and k = 2.1 x 10(-4) m2/s. With these parameter settings, for right ventricular apex (RVA) pacing, the depolarization times were realistically simulated as also shown by the wavefronts and the time needed to activate the LVFW. The moment of depolarization was used to initiate myofiber contraction in a model of LV mechanics. For both pacing situations, mid-wall circumferential strains and onset of myofiber shortening were obtained.
CONCLUSION: With a relatively simple model setup, simulated depolarization timing patterns agreed with measurements for pacing at the LVFW and RVA in an LV. Myocardial cross-fiber wave velocity is estimated to be 0.40 times the velocity along the myofiber direction (0.75 m/s). Subendocardial wave velocity is about 1.7 times faster than in the rest of the myocardium, but about 3 times slower than as found in Purkinje fibers. Furthermore, model and experiment agreed in the following respects. (1) Ventricular pacing decreased both systolic pressure and ejection fraction relative to natural sinus rhythm. (2) In early depolarized regions, early shortening was observed in the isovolumic contraction phase; in late depolarized regions, myofibers were stretched in this phase. Maps showing timing of onset of shortening were similar to previously measured maps in which wave velocity of contraction appeared similar to that of depolarization.

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Year:  2003        PMID: 14760923     DOI: 10.1046/j.1540.8167.90310.x

Source DB:  PubMed          Journal:  J Cardiovasc Electrophysiol        ISSN: 1045-3873


  14 in total

1.  Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation.

Authors:  Christian Vergara; Simone Palamara; Domenico Catanzariti; Fabio Nobile; Elena Faggiano; Cesarino Pangrazzi; Maurizio Centonze; Massimiliano Maines; Alfio Quarteroni; Giuseppe Vergara
Journal:  Med Biol Eng Comput       Date:  2014-08-24       Impact factor: 2.602

2.  Electromechanics of paced left ventricle simulated by straightforward mathematical model: comparison with experiments.

Authors:  R C P Kerckhoffs; O P Faris; P H M Bovendeerd; F W Prinzen; K Smits; E R McVeigh; T Arts
Journal:  Am J Physiol Heart Circ Physiol       Date:  2005-06-17       Impact factor: 4.733

3.  A tissue-level electromechanical model of the left ventricle: application to the analysis of intraventricular pressure.

Authors:  Virginie Le Rolle; Guy Carrault; Pierre-Yves Richard; Philippe Pibarot; Louis-Gilles Durand; Alfredo I Hernández
Journal:  Acta Biotheor       Date:  2009-10-29       Impact factor: 1.774

4.  A quantitative analysis of cardiac myocyte relaxation: a simulation study.

Authors:  S A Niederer; P J Hunter; N P Smith
Journal:  Biophys J       Date:  2005-12-09       Impact factor: 4.033

Review 5.  Whole-heart modeling: applications to cardiac electrophysiology and electromechanics.

Authors:  Natalia A Trayanova
Journal:  Circ Res       Date:  2011-01-07       Impact factor: 17.367

Review 6.  Heart-on-Chip for Combined Cellular Dynamics Measurements and Computational Modeling Towards Clinical Applications.

Authors:  Jiyoon Park; Ziqian Wu; Paul R Steiner; Bo Zhu; John X J Zhang
Journal:  Ann Biomed Eng       Date:  2022-01-17       Impact factor: 3.934

7.  A tissue-level model of the left ventricle for the analysis of regional myocardial function.

Authors:  Virginie Le Rolle; Alfredo I Hernández; Pierre-Yves Richard; Erwan Donal; Guy Carrault
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2007

8.  Computer Modelling for Better Diagnosis and Therapy of Patients by Cardiac Resynchronisation Therapy.

Authors:  Marieke Pluijmert; Joost Lumens; Mark Potse; Tammo Delhaas; Angelo Auricchio; Frits W Prinzen
Journal:  Arrhythm Electrophysiol Rev       Date:  2015-03-10

Review 9.  Mathematical modeling and simulation of ventricular activation sequences: implications for cardiac resynchronization therapy.

Authors:  Mark Potse
Journal:  J Cardiovasc Transl Res       Date:  2012-01-27       Impact factor: 4.132

Review 10.  Modeling cardiac electromechanics and mechanoelectrical coupling in dyssynchronous and failing hearts: insight from adaptive computer models.

Authors:  Nico H L Kuijpers; Evelien Hermeling; Peter H M Bovendeerd; Tammo Delhaas; Frits W Prinzen
Journal:  J Cardiovasc Transl Res       Date:  2012-01-21       Impact factor: 4.132

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