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Computational Model for Predicting Optimal Clinical Intervention in Pre-Operative Neonates with Transposition of the
Madisyn Messmore1, William DeCampli2,3,4, Alain Kassab2
1Department of Mechanical and Aerospace Engineering, University of Central Florida College of Engineering and Computer Science, Orlando, FL, USA. madisyn.messmore@ucf.edu.
Cardiovascular Engineering and Technology
|May 11, 2026
Summary
This study modeled Transposition of the Great Arteries (TGA) to find ways to improve oxygen delivery before surgery. Systemic vascular resistance and patent ductus arteriosus diameter were key factors for increasing oxygen saturation in newborns with TGA.
Area of Science:
- Cardiovascular Physiology
- Pediatric Cardiology
- Computational Biology
Background:
- Transposition of the Great Arteries (TGA) is a congenital heart defect where parallel circulations limit oxygen delivery.
- Preoperative hypoxemia in TGA often requires interventions beyond balloon atrial septostomy.
- Factors influencing shunting and oxygen delivery in TGA require further investigation.
Purpose of the Study:
- To develop a lumped parameter model (LPM) of TGA to analyze adjustable factors impacting preoperative oxygen delivery.
- To determine the sensitivity of systemic arterial oxygen saturation to various physiological parameters.
- To propose optimal preoperative treatment strategies using computational optimization.
Main Methods:
- Development of a lumped parameter model (LPM) simulating TGA hemodynamics.
- Sensitivity analysis using finite difference methods to assess parameter influence on oxygen saturation.
- Optimization using the Nelder Mead (NM) algorithm to identify ideal intervention scenarios.
Main Results:
- Systemic vascular resistance (SVR) and patent ductus arteriosus (PDA) diameter were the most sensitive factors for increasing systemic arterial oxygen saturation (Ssa).
- Increasing PDA diameter and SVR, and decreasing pulmonary vascular resistance (PVR) generally improved Ssa.
- Atrial septal defect (ASD) enlargement was not consistently the most effective parameter for improving Ssa.
Conclusions:
- Systemic and pulmonary vascular resistance significantly influence systemic oxygen delivery in TGA.
- Findings suggest interventions targeting SVR and PVR can improve preoperative oxygen saturation.
- An optimization-based tool, potentially integrating machine learning, can guide TGA preoperative management.
