Abstract 17796: Development of Computational Simulation of Pulmonary Autograft Remodeling After Ross Procedure
Introduction: Ross operation is advantageous to avoid anticoagulation and provide growth for children and young adults. Clinical outcomes have shown excellent hemodynamics, and restoration of normal life expectancy. However, the primary concern is autograft dilatation, which leads to aortic insufficiency and requires reoperation.
Hypothesis: We hypothesize that autograft remodeling in the form of increased diameter and wall thickness can be predicted using stress as a trigger with changes in stress/strain. We recently showed that autograft wall stress significantly increased immediately after the Ross operation. In this work we aimed to develop a numerical model that can predict wall growth patterns based on one-year postoperative autograft.
Methods: Magnetic resonance imaging (MRI) was obtained one year after the patient had the Ross procedure and used to reconstruct autograft geometry. Prestress was first calculated using modified updated Lagrangian formulation to eliminate any unrealistic stress brought by the pressured in-vivo geometry. Tissue growth was then triggered by a stress threshold, and then grew through the multiplicative decomposition approach. Autograft was considered to be anisotropic and hyperelastic using the general structure tensor approach with fibers. A user defined material law for LS-DYNA was developed to model tissue growth under systematic pressure.
Results: The peak first principal stress was 632.5kPa (Fig. 1Left) for the baseline and 612.2 kPa after growth (Fig. 1Right). The growth appears to reduce to stress in the sinotubular junction region.
Conclusions: We developed a numerical model to predict autograft remodeling and applied it to a postoperative patient. Further clinical follow-up data over time will be incorporated to calibrate the growth model in terms of growth threshold and growth rate. These results will eventually be used to guide surgical planning for Ross procedure and access patient-specific risk.
Author Disclosures: Y. Xuan: Research Grant; Significant; AHA postdoc fellowship grant. I. El-Hamamsy: None. F. Pierre-Mongeon: None. R. Leask: None. E.E. Tseng: Research Grant; Significant; Mentor for AHA postdoc grant, NIH R01. L. Ge: Research Grant; Significant; NIH R01.
- © 2016 by American Heart Association, Inc.