Measuring defibrillator surface potentials: The validation of a predictive defibrillation computer model. (1st November 2018)

• Record Type: Journal Article
• Authors: Tate, Jess; Stinstra, Jeroen; Pilcher, Thomas; Poursaid, Ahrash; Jolley, Matthew A.; Saarel, Elizabeth; Triedman, John; MacLeod, Rob S.
• Is Part Of: Computers in biology and medicine. Volume 102(2018)
• Page Start: 402
• Page End: 410
• Publication Date: 2018-11-01
• Subjects: Defibrillation -- Patient-specific modeling -- Limited lead selection -- Body surface mapping -- Defibrillation threshold -- Defibrillation modeling; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.08.025
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Implantable cardioverter defibrillators (ICDs) are commonly used to reduce the risk in patients with life-threatening arrhythmias, however, clinicians have little systematic guidance to place the device, especially in cases of unusual anatomy. We have previously developed a computational model that evaluates the efficacy of a delivered shock as a clinical and research aid to guide ICD placement on a patient specific basis. We report here on progress to validate this model with measured ICD surface potential maps from patients undergoing ICD implantation and testing for defibrillation threshold (DFT). We obtained body surface potential maps of the defibrillation pulses by adapting a limited lead selection and potential estimation algorithm to deal with the limited space for recording electrodes. Comparison of the simulated and measured potential maps of the defibrillation shock yielded similar patterns, a typical correlation greater than 0.9, and a relative error less than 15%. Comparison of defibrillation thresholds also showed accurate prediction of the simulations. The high agreement of the potential maps and DFTs suggests that the predictive simulation generates realistic potential values and can accurately predict DFTs in patients. These validation results pave the way for use of this model in optimization studies prior to device implantation. Highlights: Limited lead selection and body surface estimation can by applied to implantable cardioverter defibrillator potentials. Patient-specific defibrillation modeling can predict body surface potentials that agree with measurements. The defibrillation model can predict defibrillation thresholds comparable to clinically observed values. Validation in this study support the use of the patient-specific pipeline in guiding defibrillation placement strategies.