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.

Conventional transvenous defibrillation is performed with an ICD using a dual current pathway. The defibrillation energy is delivered from the RV electrode to the superior vena cava (SVC) electrode and the metallic case (CAN) of the ICD. Biventricular defibrillation uses an additional electrode placed in the LV free wall with sequential shocks to create an additional current vector. Clinical studies of biventricular defibrillation have reported a 45% reduction in mean defibrillation threshold (DFT) energy. The aim of the study was to use computational methods to examine the biventricular defibrillation fields together with their corresponding DFTs in a variety of patient derived models and to compare them to simulations of conventional defibrillation. A library of thoracic models derived from nine patients was used to solve for electric field distributions. The defibrillation waveform consisted of a LV --> SVC + CAN monophasic shock followed by a biphasic shock delivered via the RV --> SVC + CAN electrodes. When the initial voltage of the two shocks is the same, the simulations show that the biventricular configuration reduces the mean DFT by 46% (3.5 +/- 1.3 vs 5.5 +/- 2.7 J, P = 0.005). When the leading edge of the biphasic shock is equal to the trailing edge of the monophasic shock, there is no statistically significant difference in the mean DFT (4.9 +/- 1.9 vs 5.5 +/- 2.7 J, P > 0.05) with the DFT decreasing in some patients and increasing in others. These results suggest that patient-specific computational models may be able to identify those patients who would most benefit from a biventricular configuration.

No abstract

No abstract

Absrroct-Standard transvenous defibrillation is performed with implantable cardioverter defibrillators (ICD) using a dualcurrent pathway. The defibrillation energy is delivered from the right ventricle (RV) electrode to the superior vena cava (SVC) electrode and the ICD metallic housing. Clinical studies of biventricular defibrillation, which uses an additional electrode, placed on the left Ventricular (LV) free wall, in conjunction with sequential shocks, have reported a 50% reduction in defibrillation threshold (DFT) energy. The goal of our study is to use computational methods to examine the biventricular defibrillation fields together with their corresponding DFTs, and to compare to standard defibrillation. Thoracic models derived from 5 patients were used In this study. The computational models were created from segmented CT images. The electric field distribution during defibrillation was computed using the finite volume method. The critical mass hypothesis was used to define a successful shock and to calculate the DFT. Our simulations show that the biventricular lead system reduces the DFT by 30% in comparison to standard configuration in 3 of the models and increases DFT up to 12% in the remaining 2. There results are consistent with clinical reports and suggest that patient-specific computational models may be able to identify those patients who could benefit from biventricular defibrillation.