A generative 3-D statistical model using partial least squares provided more compact and precise predictions of right ventricular remodelling in tetralogy of Fallot compared to PCA regression.
Cross-Sectional (n=49)
Does a generative 3-D statistical model using PLS and CCA accurately predict right ventricular remodelling in patients with tetralogy of Fallot compared to PCA regression?
A novel 3-D statistical model using PLS and CCA can accurately predict right ventricular remodelling in patients with tetralogy of Fallot, outperforming traditional PCA regression.
Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in patients with tetralogy of Fallot, a severe congenital heart defect. The ultimate goal is to determine, among clinical variables, predictors for the RV shape from which a statistical model that predicts RV remodelling is built. Our approach relies on a forward model based on currents and a diffeomorphic surface registration algorithm to estimate an unbiased template. Local effects of RV regurgitation upon the RV shape were assessed with Principal Component Analysis (PCA) and cross-sectional multivariate design. A generative 3-D model of RV growth was then estimated using partial least squares (PLS) and canonical correlation analysis (CCA). Applied on a retrospective population of 49 patients, cross-effects between growth and pathology could be identified. Qualitatively, the statistical findings were found realistic by cardiologists. 10-fold cross-validation demonstrated a promising generalization and stability of the growth model. Compared to PCA regression, PLS was more compact, more precise and provided better predictions.
Mansi et al. (Wed,) conducted a cross-sectional in Tetralogy of Fallot (n=49). Generative 3-D model of RV growth using PLS and CCA vs. PCA regression was evaluated on Prediction of right ventricular remodelling. A generative 3-D statistical model using partial least squares provided more compact and precise predictions of right ventricular remodelling in tetralogy of Fallot compared to PCA regression.