Computational modelling of the right ventricle in repaired tetralogy of Fallot: can it provide insight into patient treatment?

Abstract

Aims: Pulmonary regurgitation (PR) causes progressive right ventricle (RV) dilatation and dysfunction in repaired tetralogy of Fallot (rToF). Declining RV function is often insidious and the timing of pulmonary valve replacement remains under debate. Quantifying the pathophysiology of adverse RV remodelling due to worsening PR may help in defining the best timing for pulmonary valve replacement. Our aim was to identify whether complex three-dimensional (3D) deformations of RV shape, as assessed with computer modelling, could constitute an anatomical biomarker that correlated with clinical parameters in rToF patients.

Methods and results: We selected 38 rToF patients (aged 10-30 years) who had complete data sets and had not undergone PVR from a population of 314 consecutive patients recruited in a collaborative study of four hospitals. All patients underwent cardiovascular magnetic resonance (CMR) imaging: PR and RV end-diastolic volumes were measured. An unbiased shape analysis framework was used with principal component analysis and linear regression to correlate shape with indexed PR volume. Regurgitation severity was significantly associated with RV dilatation (P = 0.01) and associated with bulging of the outflow tract (P = 0.07) and a dilatation of the apex (P = 0.08).

Conclusion: In this study, we related RV shape at end-diastole to clinical metrics of PR in rToF patients. By considering the entire 3D shape, we identified a link between PR and RV dilatation, outflow tract bulging, and apical dilatation. Our study constitutes a first attempt to correlate 3D RV shape with clinical metrics in rToF, opening new ways to better quantify 3D RV change in rToF.

Figures

Figure 1

Analysis pipeline. The RV was first segmented from cardiac MRI. A reference template and the deformations, which mapped that template to each RV, were then computed to quantify RV shapes. PCA on deformations was performed to extract the main shape variation features and reduce model dimension. Linear regression was finally estimated to identify the shape features related to PRVi. See text for details.

Figure 2

Left panel: 3D RV mesh of a patient overlaid on its CMR. Mid panel: 3D RV meshes of 49 patients segmented from CMR and rigidly aligned to a common co-ordinate frame. Observe the large variability in shape. Right panel: mean RV shape of the population.

Figure 3

Variations in RV shape correlated with PRVi in 38 patients. Modes 1, 12, and 15 were found significantly related to PRVi.