Patient-Specific Computational Analysis of Transvenous Defibrillation: A Comparison to Clinical Metrics in Humans

Abstract

The goal of this study is to assess the predictive capacity of computational models of transvenous defibrillation by comparing the results of patient-specific simulations to clinical defibrillation thresholds (DFT). Nine patient-specific models of the thorax and in situ electrodes were created from segmented CT images taken after implantation of the cardioverter-defibrillator. The defibrillation field distribution was computed using the finite volume method. The DFTs were extracted from the calculated field distribution using the 95% critical mass criterion. The comparison between simulated and clinical DFT energy resulted in a rms difference of 12.4 J and a 0.05 correlation coefficient (cc). The model-predicted DFTs were well matched to the clinical values in four patients (rms = 1.5 J; cc = 0.84). For the remaining five patients the rms difference was 18.4 J with a cc = 0.85. These results suggest that computational models based soley on the critical mass criterion and a single value of the inexcitability threshold are not able to consistently predict DFTs for individual patients. However, inspection of the weak potential gradient field in all nine patients revealed a relationship between the degree of dispersion of the weak field and the clinical DFT, which may help identify high DFT patients.

A computer modeling tool for comparing novel ICD electrode orientations in children and adults

“…19 Mocanu, et al created 7 FEM models of adult patients with prior ICD implants and compared predicted to clinically measured DFTs. 22 Predicted DFTs ranged from 150 to 400 volts (approximately 4-8 joules) with four of the patients having high concordance between the predicted and clinical DFTs. Patients with poor concordance had clinical features that the investigators did not include which would be anticipated to affect either the thoracic electric field (large pleural effusions) or the response to defibrillation (infarcted myocardial tissue).…”

“…13,14 Finite element modeling of defibrillation has been shown to correlate well with clinically observed defibrillation thresholds in laboriously constructed conductivity models of the adult torso. [15][16][17][18][19][20][21][22][23] These studies have validated the use of realistic models for accurate prediction of intrathoracic electric fields, allowing estimation of the defibrillation threshold voltages, currents, and impedances that would be associated with such fields. Extension of these studies to allow modeling of different electrode orientations, within variable body sizes and habitus and under anatomically variable conditions, requires simulation systems that can facilitate rapid model creation, interactive electrode placement, and clinically relevant visualization of the results.…”

“…Conductivities for the individual tissues were based on values derived from the literature as follows (all in siemens/meter): bowel gas 0.002, connective tissue 0.220, liver 0.150, kidney 0.070, skeletal muscle 0.250, fat 0.050, bone 0.006, lung 0.067, blood 0.700, myocardium 0.250. 15,17,19,22,25 The resulting finite element model incorporated a set of equations similar to those used in previous defibrillation studies. In this implementation, we assumed a linear and isotropic volume conductor model, with negligible capacitance and inductance, and applied the Galerkin finite element formulation with tri-linear interpolation.…”

Predictive modeling of defibrillation using hexahedral and tetrahedral finite element models: recent advances

“…These meshes reduce the computational burden of solving the mesh, as well as providing a more anatomical realistic representation of the volume data. Using a lookup table of conductivity values based on literature-derived values for muscle, fat, bone, lung, blood, and myocardium and other tissues [Geddes 1967; Aguel 1999; DeJongh 1999; Jorgenson 1995; Mocanu 2004], a conductivity map of the torso was formed and projected onto the computational mesh by sampling. Boundary conditions were assigned: electrode surface elements had constant potential and there was no current flow across the torso surface.…”

“…Our current research models the effects defibrillating electrical fields on anatomically derived myocardial and torso models using finite element techniques. Finite element modeling (FEM) of conductivity models of the adult torso has been shown to correlate well with clinically observed DFTs [Aguel 1999, DeJongh 1999, Jorgenson 1995, Mocanu 2004]. We extended the FEM approach to allow modeling of different electrode orientations, within variable body sizes and habitus, and under anatomically variable conditions.…”