modelling predicts ventricular tachycardia inducibility and circuit morphology: A combined clinical validation and computer modelling approach Tissue Heterogeneity (admixture of scar and myocardium) co-location with Heterogeneity in APD

Introduction: 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 reentrant VT circuits. We used a combined modelling and clinical approach in order to validate the concept. Methods and results: Non-contact electroanatomic mapping studies were performed in 7 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 reentrant 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 reentrant VT circuits. protocol during the mapping study. 11 Unipolar electrograms (UEG) derived were filtered with a band-pass filter (10Hz/300Hz and 0.5Hz/30Hz) to optimize QRS complex and T wave detections. The depolarization times were detected within the QRS window and derived from the zero crossings of the laplacian of the measured UEGs; the repolarization times were detected within the ST window for the signals. 9 The difference between the depolarization and repolarization times was used to estimate the activation recovery time (ARI) that is used as a surrogate marker for APD. 12 The APD restitution curve (APD-RC) was estimated from steady-state RV pacing (500ms) with sensed pacing extras at progressively shorter coupling interval at a decrement of 20ms till 200ms or refractory period. The APD-RC was represented by a non-linear equation using a least-squares fit to the mono-exponential function as previously detailed on experimental and clinical data: a single APD-RC was fitted for each measured point from the EAM and the maximum APD was estimated as the asymptotic APD of the APD-RC when the diastolic interval tends to infinity. 13,14 We have indeed demonstrated that additional exit points can be induced from in silico VT stimulation studies, which could be potential targets for ablation. Our results suggest that patient-specific cardiac models may offer incremental clinical benefit in terms of ventricular arrhythmia risk stratification and in the planning and delivery of ablation strategies for reentrant ventricular arrhythmias. Non-invasive body surface mapping could be incorporated to routine simple electrophysiology study to gain such personalized whole heart electro-anatomic data in order to facilitate the translation of the biophysical cardiac model processing pipeline to clinical practice. 24 A larger validation study can be performed in the secondary prevention patient cohort when simple electrophysiology study can be performed via the cardiac rhythm management device (at time of device implant) with simultaneous body surface mapping recording to acquire the personalized APD, APD-RC slopes and AC data. This coupled with scar data acquired from CMR prior to device implant could generate personalized modelling, the outcome of which can then be further validated at the time of VT ablation prospectively in this secondary prevention cohort