  Hypoplastic left heart syndrome (HLHS) is a congenital defect in which right
ventricle (RV) shape changes over time in response to altered loading
conditions as adaptations to patient's single ventricle physiology. The RV
shape can be considered as a co-indicator of the severity of underlying
tricuspid valve (TV) regurgitation in HLHS, thus the accurate prediction on RV
shape could potentially help predict valve dysfunction and timing of TV failure
to inform clinical decision. A geodesic-based predictive shape modeling
framework is proposed in this work with application to the RVs of a cohort of
15 HLHS patients as a pilot study. We show that reasonable predictions on stage
1 RV shapes can generated using pre-stage 1 RV shapes and merely two predictors
from prior clinical and demographic measures. The study results show great
promise towards further experiments for improved prediction results by
incorporating more detailed demographic and shape information into the proposed
predictive model.
