Geodesic-based Predictive Shape Modeling of the Right Ventricle in Patients with Hypoplastic Left Heart Syndrome

TL;DR

This paper introduces a geodesic-based shape modeling framework to predict the evolving shape of the right ventricle in HLHS patients, aiding clinical prognosis and decision-making.

Contribution

It presents a novel predictive shape modeling method using geodesic analysis applied to longitudinal RV data in HLHS patients.

Findings

Predictions of RV shape are feasible using pre-stage 1 data.

The method shows promise for forecasting morphological changes over time.

Future work will expand data and incorporate more variables.

Abstract

Hypoplastic left heart syndrome (HLHS) is characterized by severe underdevelopment of left ventricle requiring staged surgical reconstruction (stages) to allow the right ventricle (RV) alone to support the circulation. In this setting changes in RV size and shape over time reflect adaptations to single-ventricle physiology, dysfunction of the associated tricuspid valve (TV), and are associated with circulatory failure. As such, an accurate prediction of the RV shape of a patient would inform understanding of both RV and TV failure, as well as clinical prognosis and associated decision making. We present a geodesic-based predictive shape modeling framework applied a cohort of RVs obtained from 15 HLHS patients at three individual time points. Reasonable predictions on stage 1 RV shapes can generated using pre-stage 1 RV shapes and two predictors from prior clinical and demographic measures. Our results demonstrate the future potential for a data-driven method to predict how the morphology of the RV of an individual patient will change in size and shape over time. Future studies will seek to expand the training sample size and integrate more comprehensive demographic and morphological data into the proposed predictive model.