News

Predicting Aortic Calcification with Patient-Specific Models

Sacks 2014

Bicuspid Aortic Valve (BAV) Disease is the most common congenital heart defect. This condition affects approximately two percent of the population and is twice as common in men as women.

Normally the aortic valve, which directs the flow of blood away from the heart and to the entire body, has three flaps that open and close together. With BAV however, the valve only has two flaps. Often these flaps are distorted and do not function effectively.

BAV puts patients at a higher risk for developing aortic valve disease, which can also reduce the efficiency of the heart. Some patients with BAV go through their entire lives unaffected, while in others, aortic valve disease progresses rapidly. There are currently no ways to determine clinically which patients will acquire aortic valve disease and how fast it will occur.

With a new four-year, three million dollar R01 grant from the National Institutes of Health, Professor Michael Sacks will lead a team of researchers at UT Austin, University of Pennsylvania, Iowa State University, and Columbia University to develop advanced computational models that predict how fast aortic valve disease will occur in patients with BAV. Using powerful resources from Texas Advanced Computing Center, the research team will analyze characteristics of BAV directly from clinically obtained imaging data from patients treated at University of Pennsylvania hospitals.

“There’s a clear personalized medicine component to our project,” says Sacks, a professor of biomedical engineering, director of the Willerson Center for Cardiovascular Modeling and Simulation, and the holder of the W.A. Tex Moncrief, Jr. Endowment in Simulation-Based Engineering and Sciences.

“We will use advanced computational biomechanics, high-resolution 3D reconstructions and detailed knowledge of structure-mechanics of congenitally defective heart valves to develop a new approach, which will allow us to estimate the mechanical behavior and deformations on patient-specific imaging data.”

The overall goal of the project is to identify geometric features that lead to a high risk of aortic stenosis development based on a thorough understanding of patient-specific BAV characteristics unique to the disorder.