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.