Advancing Surgical Solutions for Pulmonary Hypertension Through Computational Modeling

Our project focuses on improving surgical treatments for severe Pulmonary Arterial Hypertension (PAH), a life-threatening pediatric disease characterized by high blood pressure in the lungs. PAH can progressively lead to heart failure and may be fatal without effective treatment. While medication can help, severe cases often require lung transplants. However, considering the shortage of organ donors and the related complications, an alternative surgical procedure, known as the Potts shunt, offers hope. This procedure connects the pulmonary artery to the aorta, easing pressure on the right side of the heart. Despite its promise, the Potts shunt has had varied success rates. Our research aims to understand and improve this procedure through advanced simulations of blood flow dynamics. In collaboration with leading hospitals in France and the US, we will analyze different surgical techniques and shunt designs. By developing state-of-the-art virtual human twins with both mechanistic modeling and machine learning, we aim to refine these simulations for better accuracy and faster results. Ultimately, this project seeks to enhance the success rate of the Potts shunt surgery, providing a viable alternative to lung transplantation for many PAH patients, thereby improving their quality of life and reducing the burden on healthcare systems.