Biophysical modelling predicts ventricular tachycardia inducibility and circuit morphology: A combined clinical validation and computer modelling approach

Abstract : Computational modelling of cardiac arrhythmogenesis and arrhythmia maintenance has made a significant contribution to the understanding of the underlying mechanisms of arrhythmia. We hypothesized that a cardiac model using personalized electro-anatomical parameters could define the underlying ventricular tachycardia (VT) substrate and predict re-entrant VT circuits. We used a combined modelling and clinical approach in order to validate the concept. Non-contact electroanatomic mapping studies were performed in seven patients (5 ischemics, 2 non-ischemics). Three ischemic cardiomyopathy patients underwent a clinical VT stimulation study. Anatomical information was obtained from cardiac magnetic resonance imaging (CMR) including high-resolution scar imaging. A simplified biophysical mono-domain action potential model personalized with the patients’ anatomical and electrical information was used to perform in silico VT stimulation studies for comparison. The personalized in silico VT stimulations were able to predict VT inducibility as well as the macroscopic characteristics of the VT circuits in patients who had clinical VT stimulation studies. Patients with positive clinical VT stimulation studies had wider distribution of action potential duration restitution curve (APD-RC) slopes and APDs than patient 3 with a negative VT stimulation study. The exit points of re-entrant VT circuits encompassed a higher percentage of the maximum APD-RC slope compared to the scar and non-scar areas, 32%, 4% and 0.2% respectively. Conclusions: VT stimulation studies can be simulated in silico using a personalized biophysical cardiac model. Myocardial spatial heterogeneity of APD restitution properties and conductivity may help predict the location of crucial entry/exit points of re-entrant VT circuits.
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Journal of Cardiovascular Electrophysiology, Wiley, 2016, 27 (7), pp.851-860. 〈10.1111/jce.12991〉
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Zhong Chen, Rocío Cabrera-Lozoya, Jatin Relan, Manav Sohal, Anoop Shetty, et al.. Biophysical modelling predicts ventricular tachycardia inducibility and circuit morphology: A combined clinical validation and computer modelling approach. Journal of Cardiovascular Electrophysiology, Wiley, 2016, 27 (7), pp.851-860. 〈10.1111/jce.12991〉. 〈hal-01301426〉

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