Daniel Mocanu

A comparison of biventricular and conventional transvenous defibrillation: a computational study using patient derived models.

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. (PACE 2004; 27:586–593)

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

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.

Computational Modeling of Arterial Blood Flow

Recently, there is a growing interest in developing numerical methods and tools to investigate the hemodynamics of the arterial flow, and to understand its influence on the transport of solutes (e.g., oxygen), nutrients, etc. As arteries morphology is complex and patient-related, medical data based reconstruction of the geometry may be utilized to generate realistic computational domains. The blood flow is then investigated by finite element method (FEM) for a range of flow parameters. The flow patterns thus obtained may be utilized for vascular surgery training, planning and intervention, to investigate atherosclerosis genesis, in drug targeting, etc.

A patient-specific computational study of transvenous defibrillation [segmented CT images]

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 clinically determined defibrillation metrics. Solutions for seven patient-specific models have been completed. The 3-D models of the thorax and in situ electrodes were created from segmented CT images taken shortly after implant. Each of the 3-D models was created by defining each voxel in the segmented data set as a volume element in the computational model. 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 determine the defibrillation metrics from the calculated field distribution. Simulated defibrillation thresholds yielded good estimates of the clinically determined thresholds in 4 of the 7 patients examined. The model-predicted impedances correlate well with the clinical measurements. These results are promising and provide preliminary support to the potential utility of this modeling approach for patient-specific surgical planning of cardioverter defibrillator implantation and for evaluating new electrode configurations.

Biventricular defibrillation with sequential shocks using patient-derived computational models

Standard transvenous defibrillation is performed with implantable cardioverter defibrillators (ICD) using a dual-current 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. These 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.

Patient-Specific Simulation of Internal Defibrillation

The objective of this study is to investigate the predictive capacity of computational models of electrical defibrillation by comparing the results of patient-specific simulations to clinically determined defibrillation metrics. Finite volume models of the thoracic conductive anatomy and in situ electrodes were constructed for seven patients who received implantable defibrillators. These models were based on segmented X-ray CT images taken shortly after implant. The models were solved for electric field (current density) distributions corresponding to a defibrillation shock. The defibrillation parameters were calculated from these distributions based on critical mass and inexcitability criteria for successful defibrillation. Preliminary results show good agreement between clinical and simulated thresholds for four of the seven patients modeled to date. The defibrillation parameters for the remaining three patients are underestimated. The correspondence between the predicted and measured defibrillation metrics observed in four of the seven patients is encouraging and provides preliminary support to the potential utility of the modeling approach. This approach may allow for patient specific presurgical planning, as well as provide a convenient computational testbed for evaluating new electrode configurations. Although these results are promising additional subjects are needed to further validate the modeling method.

A dynamic model for cardiac defibrillation

A detailed two-dimensional (2-D) finite element model (FEM) for the electroconductive anatomy of a human thorax was formulated to investigate the unsteady diffusion of the electromagnetic field that occurs during defibrillation. An equivalent electric circuit simulating a defibrillator is solved to generate the analytic form of the defibrillation current, that is further used as input (boundary condition) to the field problem. Specific insights in the defibrillation process, not apparent through an electrokinetic analysis, such as dynamic patterns of current density that irrigates the heart and the important role played in the diffusion process by chest wall, intercostal muscles and interstitial fluid, were evidenced. Our results lead to a deeper understanding of the anatomic structure response to defibrillation and suggest that a different approach to its optimization may be needed.