The objective of this study was to measure the defibrillation threshold (DFT) associated with different electrode placements using a three-dimensional anatomically realistic finite element model of the human thorax. Coil electrodes (Endotak DSP, model 125, Guidant/CPI) were placed in the RV apex along the lateral wall (RV), withdrawn 10 mm away from the RV apex along the lateral wall (RVprox), in the RV apex along the anterior septum (RVseptal), and in the SVC. An active pulse generator (can) was placed in the subcutaneous prepectoral space. Five electrode configurations were studied: RV-->SVC, RVprox-->SVC, RVSEPTAL-->SVC, RV-->Can, and RV-->SVC + Can. DFTs are defined as the energy required to produce a potential gradient of at least 5 V/cm in 95% of the ventricular myocardium. DFTs for RV-->SVC, RVprox-->SVC, RVseptal-->SVC, RV-->Can, and RV-->SVC + Can were 10, 16, 7, 9, and 6 J, respectively. The DFTs measured at each configuration fell within one standard deviation of the mean DFTs reported in clinical studies using the Endotak leads. The relative changes in DFT among electrode configurations also compared favorably. This computer model allows measurements of DFT or other defibrillation parameters with several different electrode configurations saving time and cost of clinical studies.

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Three-dimensional (3-D) finite element torso models are widely used to simulate defibrillation field quantities, such as potential, gradient, and current density. These quantities are computed at spatial nodes that comprise the torso model. These spatial nodes typically number between 10(5) and 10(6), which makes the comprehension of torso defibrillation simulation output difficult. Therefore, the objective of this study is to rapidly prototype software to extract a subset of the geometric model of the torso for visualization in which the nodal information associated with the geometry of the model meets a specified threshold value (e.g., minimum gradient). The data extraction software is implemented in PROLOG, which is used to correlate the coordinate, structural, and nodal data of the torso model. A PROLOG-based environment has been developed and is used to rapidly design and test new methods for sorting, collecting, and optimizing data extractions from defibrillation simulations in a human torso model for subsequent visualization.

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