An in-silico analysis of the effect of changing activation wavefronts on voltage amplitudes in patients with heart failure
NGUYÊN, Uyên Châu
Maastricht University [Maastricht]
Center for Computational Medicine in Cardiology [Lugano]
Maastricht University [Maastricht]
Center for Computational Medicine in Cardiology [Lugano]
POTSE, Mark
Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
IHU-LIRYC
Institut de Mathématiques de Bordeaux [IMB]
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Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
IHU-LIRYC
Institut de Mathématiques de Bordeaux [IMB]
NGUYÊN, Uyên Châu
Maastricht University [Maastricht]
Center for Computational Medicine in Cardiology [Lugano]
Maastricht University [Maastricht]
Center for Computational Medicine in Cardiology [Lugano]
POTSE, Mark
Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
IHU-LIRYC
Institut de Mathématiques de Bordeaux [IMB]
Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
IHU-LIRYC
Institut de Mathématiques de Bordeaux [IMB]
KRAUSE, Rolf
Faculty of Informatics [Lugano]
Center for Computational Medicine in Cardiology [Lugano]
< Reduce
Faculty of Informatics [Lugano]
Center for Computational Medicine in Cardiology [Lugano]
Language
en
Communication dans un congrès
This item was published in
EHRA Europace - Cardiostim 2017, 2017-06-18, Vienna.
English Abstract
Background: Voltage amplitudes are commonly used for the detection of non-excitable tissue, but may be influenced by the propagation of the activation wavefront with respect to the recording electrode. Objective: The aim ...Read more >
Background: Voltage amplitudes are commonly used for the detection of non-excitable tissue, but may be influenced by the propagation of the activation wavefront with respect to the recording electrode. Objective: The aim of this study was to investigate the influence of changing activation wavefronts on unipolar voltageamplitudes (UnipV) in-silico using tailored models of patients with heart failure.Methods: Five patient-tailored bidomain models were created. Propagating wavefronts and electrograms were computedwith state-of-the-art techniques. Simulations were performed with the Propag-5 software on 2304 cores of the Bullxcluster "Curie" (TGCC, CEA, France). The baseline simulation was fitted on geometrical and electro-anatomic mappingdata during intrinsic rhythm from heart failure patients. Fibrosis was not incorporated in the simulations allowing only onesource of variation on UnipV. UnipV from simulations of single point right ventricular (RV) pacing and left ventricular (LV)pacing were compared at the LV endocardium and epicardium with the baseline simulation (intrinsic rhythm) using pairedanalyses.Results: A total of 26,872 paired endocardial (5,374±1,165 per patient) and 51,756 epicardial electrograms(10,351±2,068 per patient) were analysed. At baseline, three patients had a left bundle branch block (LBBB) and twopatients a non-specific intraventricular conduction defect. The correlation of UnipV between baseline and pacing waspoor for both endocardial (RV pacing: R=0.38, LV pacing: R=0.30) as well as epicardial UnipV (RV pacing: R=0.30, LVpacing: R=0.13). The mean absolute change in UnipV between baseline and RV and LV pacing was respectively 3.9±1.2mV and 3.9±1.2 mV (both 31% of baseline) for the endocardium, and 5.8±5.8 mV and 6.4±5.4 (36% and 41% ofbaseline) for the epicardium. RV pacing resulted on average in lower endocardial UnipV in 1 patient, and higher UnipV in4 patients, while lower epicardial UnipV were observed in 4 patients, and higher UnipV in 1 patient (all p<0.001). LVpacing resulted in lower endocardial UnipV in 2 patients and higher UnipV in 3 patients, while lower epicardial UnipV wasobserved in 4 patients and higher UnipV in 1 patient (all p<0.001). There was no linear correlation between activationtime and UnipV, neither for the endocardial nor the epicardial measurements (all R=-0.05-0.05). Simulation bullseye plotsof the activation sequence (left panel) and corresponding UnipV distribution (right panel) of the LV endocardium duringbaseline (in this patient LBBB) and pacing is shown in the figure. Note that the UnipV distribution changes substantiallywith different activation wavefronts.Conclusion: UnipV’s are strongly influenced by changing activation wavefronts, influencing the characterization of lowvoltageareas and possibly the localization of non-excitable tissue. There is no linear correlation between the activationtime and UnipV.Read less <
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