# Towards Simulation-based Ventricular Tachycardia Risk Assessment in repaired Tetralogy of Fallot patients

Olivier Crabbé, Pierre L'eplattenier, Karim El Houari, Christelle Grivot, Francis Bessière, Raphaël Martins, Pascal Haigron

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Olivier Crabbé, Pierre L'eplattenier, Karim El Houari, Christelle Grivot, Francis Bessière, et al. "Towards Simulation-based Ventricular Tachycardia Risk Assessment in repaired Tetralogy of Fallot patients." Multidisciplinary Biomechanics Journal, 2025, 50th congress of the..., 10.46298/mbj.16175. hal-05191727v2

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Submitted on 23 Oct 2025

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# Towards Simulation-based Ventricular Tachycardia Risk Assessment in Repaired Tetralogy of Fallot Patients

Olivier Crabbé<sup>a, b*</sup>, Pierre L'Eplattenier<sup>a</sup>, Karim El Houari<sup>a</sup>, Christelle Grivot<sup>a</sup>, Francis Bessière<sup>c</sup>, Raphaël Martins<sup>b</sup>, Pascal Haigron<sup>b</sup>

<sup>a</sup> Ansys Inc., Canonsburg, PA, USA

<sup>b</sup> Université de Rennes, CHU Rennes, LTSI, Inserm - U1099, Rennes, France

<sup>c</sup> Université Claude Bernard Lyon 1, Hospices Civils de Lyon, LabTau, Inserm - U1032, Lyon, France

* Corresponding author: olivier.crabbe@ansys.com

Received date: 04/04/2025

Accepted date: 27/06/2025

Publication date: 27/10/2025

**Keywords:** ventricular tachycardia, electrophysiology, simulation, reaction-eikonal

© 2025 The Authors

Licence CC-BY 4.0

Published by Société de Biomécanique

## 1. Introduction

Tetralogy of Fallot (ToF) is the most common congenital heart defect affecting 1 in 2,800 live births. Surgical repair of ToF has a 97.1% 10-year survival rate but results in a 14% occurrence rate of Ventricular Tachycardia (VT) in the 30 following years. This results in a 0.15% Sudden Cardiac Death (SCD) rate in the repaired ToF (rToF) population. 90% of these VTs are linked to an isthmus consistently located in the same region (Bessière et al. 2023). VT risk stratification is a relevant clinical challenge to limit rToF SCD rates. The current solution is Programmed Ventricular Stimulation (PVS), an invasive and costly procedure where the patient's heart is paced via catheter to induce VT. Patient-specific cardiac electrophysiology (EP) computer models are a promising tool that can provide deeper insight into a patient's pathology using non-invasive data. However, many simulations are often required to overcome EP parameter uncertainty. When facing multiple simulations, Reaction-Eikonal (RE) EP models are preferred as they require less computational power than the gold-standard Reaction-Diffusion (RD) models. Said RE models only allow idealized VT simulations, but these have been shown to bring information comparable to RD models (Campos et al. 2022). We present an algorithm that predicts if pacing protocols will induce idealized VT in a given isthmus of a RE model. By applying this method to a cohort of nine rToF patients, we can extract a metric that could be used in VT risk stratification.

## 2. Methods

### 2.1 Patient Data

Patients included in this study are adult rToF patients, having had PVS study and having an available Cardiac CT scan with visible calcifications around the typical rToF scar patches. Four patients with positive PVS outcomes and five with negative PVS outcomes were selected.

### 2.2 Model Construction

Biventricular EP Eikonal simulation models were generated using the pyAnsys-Heart (Hoeijmakers et al., 2023) package for LS-DYNA simulation (Ansys Inc., Canonsburg, PA, USA), after manual segmentation of the CT data. The isthmus located between the ventricular septal defect and the pulmonary annulus was automatically labelled.

### 2.3 Predictor of Pacing Protocol Outcome (3PO)

We define a pacing protocol as two stimuli at a given pacing site, separated by a pacing interval. The outcome prediction is based on five conditions involving wave travel times across the heart, the pacing interval and the healthy tissue and isthmus refractory periods ($RP_H$ and $RP_{BZ}$ respectively). Four specific Eikonal simulations allow us to determine the required travel times for all possible pacing sites in the model, meaning all points of the healthy tissue. A subset of these sites is selected in post-processing to limit redundancy. For each site, having the associated travel times, we can use the five conditions
to deduce the ranges of RP<sub>H</sub> and RP<sub>BZ</sub> that would result
in VT if we were to pace that site at a given interval.

## 2.4 VT Risk Metric

We use 3PO to run various pacing protocols on slightly
different versions of the patient's model, to account for
parameter and geometrical uncertainty. Later, for each
patient, the sum of the VT-inducing RP<sub>BZ</sub> ranges across all
pacing sites and models is computed and used to assess risk.

# 3. Results and discussion

3.1 3PO Accuracy

One of the patient models was used to evaluate 3PO
accuracy. 12,500 model variants were created with var-
ying scar size, conduction velocity and orthotropy ratio
in the healthy tissue and the isthmus. 5000 pacing sites
and 1 pacing interval were studied on each model. The
accuracy assessment was done by running 188,263 RE
simulations, one for each VT-inducing pacing protocol
predicted by 3PO across the models. RP<sub>BZ</sub> values for each
simulation were set in the middle of the corresponding
VT-inducing range. 187,215 (99.44%) predicted arrhyth-
mias were successfully induced in direct simulations.

3.2 Assessing VT Risk

50 model versions, 3000 pacing sites and 9 pacing intervals per patient were used for the analysis. Varying parameters were scar size and conduction velocity in the healthy tissue. The VT risk metric results are shown on Figure 1. The same parameter sets and pacing intervals were used for all patients. Risk metric values range from 3 to 24 and from 21 to 64 million of milliseconds for negative and positive PVS patients respectively. Two patients of each group are overlapping in the 21 to 25 million range.

3.3 Discussion

Encouraging results in both 3PO accuracy and the risk
assessment attempt were found. A more thorough vali-
dation must be made on 3PO on both ends of the pre-
dicted arrhythmogenic RP<sub>BZ</sub> range instead of just a single
value in the middle. Failing cases can be fixed by using a
smaller simulation timestep or filtered by constraining the
analysis to physiological cases. Regarding risk assessment,
an improvement could be personalization of the tested
parameter ranges using electrocardiograms. Also, the
presented metric calculation needs filtering of unrealistic
scenarios. Threshold values need to be set for low, inter-
mediate and high risk based on larger patient cohorts.

<figure><img src="image_2.png" /><figcaption>Figure 1. Sum of the VT-inducing RP<sub>BZ</sub> ranges for each patient. PVS: Programmed Ventricular Stimulation.</figcaption></figure>

4. Conclusions

We present the first non-invasive assessment of Fallot
related VT using computational models across a wide
range of parameter combinations. Although imperfect,
the presented methods show encouraging results that
could prove useful in many applications.

Contributor Roles

OC: Conceptualization, Software, Methodology, Investigation, Visualization, Writing- original draft; PLE: Software, Methodology; KEH: Conceptualization, Writing- review & editing; CG: Conceptualization; FB: Conceptualization, Data curation, Validation; RM: Conceptualization, Data curation, Validation; PH: Supervision, Writing- review & editing.

References

Bessière, F., Waldmann, V., Combes, N., Metton, O., Dib, N., Mondésert, B., ... Khairy, P. (2023). Ventricular arrhythmias in adults with congenital heart disease, Part I: JACC State-of-the-Art Review. *Journal of the American College of Cardiology*, 82(11), 1108–1120.
https://doi.org/10.1016/j.jacc.2023.06.034

Campos, F. O., Neic, A., Mendonca Costa, C., Whitaker, J., O'Neill, M., Razavi, R., ... Bishop, M. J. (2022). An automated near-real time computational method for induction and treatment of scar-related ventricu- lar tachycardias. *Medical Image Analysis*, 80, 102483.
https://doi.org/10.1016/j.media.2022.102483

Hoeijmakers, M., Houari, K. E., Ye, W., L'Eplattenier, P., Nagy, A., Benson, D., & Rochette, M. (2023). pyheart- lib: A python library for LS-DYNA multi-phys- ics heart simulations. In O. Bernard, P. Clarysse, N. Duchateau, J. Ohayon, & M. Viallon (Eds.), *Functional Imaging and Modeling of the Heart* (pp. 565– 574). Cham: Springer Nature Switzerland. https:// doi.org/10.1007/978-3-031-35302-4_58