Mar 24, 2016 | By Benedict

Researchers from the Department of Radiology at the University of Washington, Seattle, have developed a technique for 3D printing aortic valve models which could help reduce the risk of complications from transcatheter aortic valve replacement (TAVR).

For many patients suffering from aortic stenosis, TAVR represents a safe alternative to more extreme surgery. The relatively non-invasive procedure involves repairing the valve without removing the damaged part. A fully collapsible replacement valve is delivered to the site via a catheter, in a process similar to that of placing a stent within an artery.

Unfortunately, complications can still occur during TAVR, with paravalvular aortic regurgitation (PAR) a particularly common problem. PAR can occur when an incorrectly fitting prosthetic valve is installed, or if a circumferential seal is not achieved. Since the aortic root is a complicated area of the human anatomy, and can come in different shapes and sizes, it is often difficult for doctors to know what size of prosthetic valve they should use.

At present, cardiac CT, transthoracic echocardiography (TTE), and/or transesophageal echocardiography (TEE) are used to “size up” a patient’s aortic valve. These imaging techniques are effective to a certain degree, but the 2D images which they produce are limited in their accuracy and effectiveness. The University of Washington researchers hypothesized that a novel 3D printed model of the aortic valve could be used to help doctors to more accurately predict how a specific aortic valve would react to a prosthesis of a certain size and shape, and whether that patient would be at a high risk of post-procedural PAR.

To test their hypothesis, the research team set up an experiment in which they could test the effectiveness of 3D printed models. During the course of the study, lead author and radiology resident Beth Ripley, M.D., alongside a team of experts from the Department of Radiology, identified eight patients who each experienced PAR after TAVR, matching them with eight similar patients—in terms of age, sex, and size of implanted valve—who did not suffer from the complication after undergoing TAVR.

Imaging data was obtained for all of the patients, before a stereolithography 3D printer was used to create flexible aortic models, and a material extrusion 3D printer used to make hard plastic valve models. To ensure that each 3D printed model was of an accurate size, measurements were compared with those of the cardiac CT images.

Once the models had each been 3D printed and assembled, the researchers used a cleverly devised method for assessing the presence of PAR. First, the valve models for the PAR-suffering patients were each affixed with 3D printed models of their unsuccessful, PAR-inducing implants. The researchers used a simple photography technique to detect light projecting through the left ventricular outflow tract, a surefire indicator of PAR. The same procedure took place with the PAR-free patients and their respective implants, where less or no light was to be expected.

Sure enough, the light transmission test successfully identified PAR in six of nine patients (66%), and correctly excluded PAR in five of the seven patients (71%). One of the eight control patients transpired to be suffering from PAR after all, taking the tally of PAR sufferers from eight to nine, and decreasing the number of control patients from eight to seven.

The general accuracy of the tests is promising for the team, since it suggests that doctors could, in future, make 3D printed models of patients’ aortic valves and potential implants before undergoing TAVR. Then, should the light transmission test indicate potential PAR, the doctor and patient would be able to consider other treatments. Should, however, the test not indicate the potential for PAR, the doctor could be relatively confident in the appropriateness of the procedure for the patient in question.

Assisting Beth Ripley with the research were Tatiana Kelil, Michael K. Cheezum, Alexandra Goncalves, Marcelo F. Di Carli, Frank J. Rybicki, Mike Steigner, Dimitrios Mitsouras, and Ron Blankstein. The research paper documenting their findings has been published in the Journal of Cardiovascular Computed Tomography.



Posted in 3D Printing Application



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