Dynamic computational modeling of congenital aortic valvar disease

The bicuspid aortic valve is the most common form of congenital heart disease, present in 0.5% to 2% in humans, with high variability in the progression towards valve dysfunction, i.e. stenosis and/or regurgitation.

Surgical strategies in adolescents and young adults, whether aortic valve repair or replacement, vary by institutional preference and expertise with no optimal option.

A repair approach aiming to restore the normal geometry of the complex three-dimensional aortic root has become popular in adult patients. But, this approach has limited objective data in the adolescent and young adult patients to guide repair.

There is a critical need for diagnosing the morphologies that dictate well- versus dysfunctional aortic valves by applying patient-specific imaging assessment for surgical aortic valve repair planning and peri-operative evaluation in adolescents and young adults with malformed aortic valves.

Echocardiogram has brought new knowledge of the pathophysiology of aortic valvar disease, but this technique has limited predictive power for outcomes of surgery.

The research aims to determine how incremental morphological changes impact the aortic valve function, hemodynamics, and tissue biomechanics using a computational biomechanical aortic valve model to guide improvements in aortic valve repair techniques.

These aims will help to understand the benchmark for repair to reconstruct a malformed aortic root into a well-functioning trileaflet or bileaflet valve.