Computational Model Accurately Simulates Shunt Performance to Prevent Repeat Surgeries

Treatment typically involves the surgical placement of shunts to divert the fluid, but this approach often leads to complications, including infections and repeated procedures. Tens of thousands of shunt surgeries are performed each year in the U.S. alone, with many patients requiring repeat surgeries due to obstruction or infection. These issues are often tied to the fact that conventional shunt tubing mimics household plumbing, lacking consideration for the brain’s complex fluid dynamics. Now, researchers have developed a new computational model that simulates shunt performance by integrating brain anatomy, fluid flow, and biomolecular transport, to enable custom shunt designs tailored to individual patients.

The model named BrainFlow was developed by researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (Cambridge, MA, USA) after they recognized the absence of a universally accepted fluid flow model for the brain’s ventricular space. BrainFlow combines detailed anatomical and physiological features of the brain to simulate the flow of cerebrospinal fluid in the presence of shunt implants. It incorporates patient-specific medical imaging and mimics pulse-induced flow to reflect realistic cerebrospinal fluid dynamics. This allows for insights into optimal shunt design, placement, and materials.

In their study published in the Proceedings of the National Academy of Sciences, the researchers pursued the problem from both a design and material science perspective, considering biomimetic solutions and anti-biofouling coatings to reduce the risk of complications. The team is now using the BrainFlow model to test various shunt designs and calculate their performance efficacy. By providing detailed simulations, the model could lead to smoother integration of optimized, patient-specific devices into the brain, reducing complications and enhancing quality of life. Looking ahead, the researchers plan to further evaluate the model’s accuracy in predicting long-term outcomes and refine it for broader clinical use.

“We believe that our model, combined with novel geometries and materials improvements such as anti-biofouling coatings developed in my lab, could lead to smoother integration of optimized, patient-specific medical devices into patients’ brains, with less likelihood of complications, and a better quality of life,” said SEAS Professor Joanna Aizenberg.

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