Biophysical Modeling Predicts Ventricular Tachycardia Inducibility and Circuit Morphology: A Combined Clinical Validation and Computer Modeling Approach.

INTRODUCTION: Computational modeling 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 modeling 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. The patients with positive clinical VT stimulation studies had wider distribution of action potential duration restitution curve (APD-RC) slopes and APDs than the patient 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.

Image-Based Biophysical Simulation of Intracardiac Abnormal Ventricular Electrograms.

GOAL: In this paper, we used in silico patient-specific models constructed from three-dimensional delayed-enhanced magnetic resonance imaging (DE-MRI) to simulate intracardiac electrograms (EGM). These included electrically abnormal EGM as these are potential radiofrequency ablation (RFA) targets. METHODS: We generated signals with distinguishable macroscopic normal and abnormal characteristics by constructing MRI-based patient-specific structural heart models and by solving the simplified biophysical Mitchell-Schaeffer model of cardiac electrophysiology (EP). Then, we simulated intracardiac EGM by modeling a recording catheter using a dipole approach. RESULTS: Qualitative results show that simulated EGM resemble clinical signals. Additionally, the quantitative assessment of signal features extracted from the simulated EGM showed statistically significant differences (p 0.0001) between the distributions of normal and abnormal EGM, similarly to what is observed on clinical data. CONCLUSION: We demonstrate the feasibility of coupling simplified cardiac EP models with imaging data to generate intracardiac EMG. SIGNIFICANCE: These results are a step forward in the direction of the preoperative and noninvasive identification of ablation targets to guide RFA therapy.

Spatial correlation of action potential duration and diastolic dysfunction in transgenic and drug-induced LQT2 rabbits.

BACKGROUND: Enhanced dispersion of action potential duration (APD) is a major contributor to long QT syndrome (LQTS)-related arrhythmias. OBJECTIVE: To investigate spatial correlations of regional heterogeneities in cardiac repolarization and mechanical function in LQTS. METHODS: Female transgenic LQTS type 2 (LQT2; n = 11) and wild-type littermate control (LMC) rabbits (n = 9 without E4031 and n = 10 with E4031) were subjected to phase contrast magnetic resonance imaging to assess regional myocardial velocities. In the same rabbits' hearts, monophasic APDs were assessed in corresponding segments. RESULTS: In LQT2 and E4031-treated rabbits, APD was longer in all left ventricular segments (P < .01) and APD dispersion was greater than that in LMC rabbits (P < .01). In diastole, peak radial velocities (Vr) were reduced in LQT2 and E4031-treated compared to LMC rabbits in LV base and mid (LQT2: -3.36 ± 0.4 cm/s, P < .01; E4031-treated: -3.24 ± 0.6 cm/s, P < .0001; LMC: -4.42 ± 0.5 cm/s), indicating an impaired diastolic function. Regionally heterogeneous diastolic Vr correlated with APD (LQT2: correlation coefficient [CC] 0.38, P = .01; E4031-treated: CC 0.42, P < .05). Time-to-diastolic peak Vr were prolonged in LQT2 rabbits (LQT2: 196.8 ± 2.9 ms, P < .001; E4031-treated: 199.5 ± 2.2 ms, P < .0001, LMC 183.1 ± 1.5), indicating a prolonged contraction duration. Moreover, in transgenic LQT2 rabbits, diastolic time-to-diastolic peak Vr correlated with APD (CC 0.47, P = .001). In systole, peak Vr were reduced in LQT2 and E4031-treated rabbits (P < .01) but longitudinal velocities or ejection fraction did not differ. Finally, random forest machine learning algorithms enabled a differentiation between LQT2, E4031-treated, and LMC rabbits solely based on "mechanical" magnetic resonance imaging data. CONCLUSIONS: The prolongation of APD led to impaired diastolic and systolic function in transgenic and drug-induced LQT2 rabbits. APD correlated with regional diastolic dysfunction, indicating that LQTS is not purely an electrical but an electromechanical disorder.