A new computational modeling technique for the heart’s mitral valve will help surgeons better customize each patient’s heart surgery.
Michael Sacks, the project’s lead researcher and a biomedical engineering professor, said the technique will allow surgeons to model the mitral valve before surgery and simulate different mitral valve surgical procedures to predict outcomes.
“This is the first (computational modeling technique) that can take patient-specific information of the mitral valve and create a patient-specific model of (the valve),” Sacks said.
Blood is pumped from the left atrium to the left ventricle of the heart, and the mitral valve separates the two chambers, said Mark Pirwitz, chief of the division of cardiology at Dell Medical School.
“If the mitral valve leaks, regurgitation can occur, where blood flows backwards,” Pirwitz said. “(This) increases heart pressure and can lead to heart dysfunction, heart failure and cause the heart muscle to weaken over time.”
Pirwitz said depending on the underlying issue, there are a number of different surgical techniques for repairing a regurgitating mitral valve.
However, the problem with these treatments is surgeons cannot tell how patients will respond post-surgery, Sacks said.
“Some patients respond very well, while others re-experience mitral valve regurgitation post surgery,” Sacks said.
This is because surgeons currently have no way of accounting for the natural variations in the structure of the mitral valve for each patient, said Amir Khalighi, a research assistant at the UT Institute for Computational Engineering and Science.
The structure of the mitral valve affects how it responds to stress, and there are currently no techniques available to allow surgeons to model the valve’s response, Pirwitz said.
“As of now, surgical decisions for mitral valve repair are based on the surgeon’s experience and through an analysis of the image of the valve, which is obtained from echocardiography imaging,” Pirwitz said.
Echocardiography imaging uses sound waves to create images of the heart. To help improve surgical outcomes, the computational modeling technique will take these pre-surgical images of the mitral valve and turn it into a model, Sacks said.
“The model simulates the structure of the valve and shows how it closes (in its pre-surgical state),” Sacks said. “From there, (the model) can simulate different (surgical) scenarios and choose the scenario that predicts the best outcome.”
The model, developed in collaboration with researchers from the University of Pennsylvania and Georgia Tech, is not in clinical use right now but is being optimized for usage in a hospital setting, Khalighi said.
“So far, the model has shown high predictive power,” Khalighi said of the computational model, which is currently being tested on past clinical data.
With the complexities and challenges of mitral valve surgery, Pirwitz said he welcomes the new modeling technique.
“Anything that we can do to impact the outcomes of our patients and any tools we can use to determine the likelihood of success will be invaluable,” Pirwitz said.