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Computational Model of Heart’s Mitral Valve to Predict Surgery Outcomes

Mitral valve repairs are complex surgeries that require particular attention to each patient’s unique anatomy, type of regurgitation such as in Carpentier’s classification, and other factors.

Recently, transcatheter mitral valve implants have started to become popular, but they’re very difficult to place so as not to obstruct nearby blood flow. Bioengineers at The University of Texas at Austin, Penn Medicine and Georgia Tech have developed a computational modeling method for the movement of mitral valve flaps, which may allow physicians to simulate different surgical techniques and predict which will work out the best.

Mitral valves are hard to study in vivo because they’re deep inside the body, move fast, and are quite complex mechanically. So deciding just how to fix them in optimal manner is a challenge. Surgeons can disagree on what would be appropriate for a given patient, but a model of the mitral valve may help.

The researchers gathered highly accurate data on the anatomy and motion of a variety of mitral valves, taken from 3D echocardiography scans of real patients. These were transferred into the computer and turned into software models that can be modified and played with in a variety of ways. “Our models combined the complete 3D geometry of the mitral valve in the open and closed states, making possible an unparalleled level of predictive accuracy,” said Michael Sacks, one of the researchers on the project. “To model the MV leaflets, we then integrated into the MV models the structure and mechanical properties of the internal constituents, such as the collagen fibers which make up most of the valve, to develop attribute-rich complete MV models.”

It is hoped that these models will be eventually validated for effective use in the clinical setting, as current treatments of mitral valve disease are often unpredictable and result in patients returning due to recurring symptoms.

Details

  • Austin, TX 78712, USA
  • The University of Texas