Bioengineers in Harvard University’s John A Paulson School of Engineering and Applied Sciences (SEAS) have developed a new computational model that combines brain anatomy, fluid flow and biomolecular transport dynamics to simulate the performance of brain shunts with pinpoint accuracy, with the ultimate aim of creating of custom shunts that are tailored to specific patients’ anatomy and needs.
Millions of people worldwide suffer from hydrocephalus – a build-up of excess cerebrospinal fluid in the brain. Treatment usually involves surgical placement of shunts to divert fluid away, but this procedure often leads to complications, infections and multiple re-treatments. Tens of thousands of shunt procedures are performed annually in the USA – many of which are repeat surgeries due to the inserted devices becoming blocked or obstructed, or the patient suffering an infection.
‘Some elderly patients told me they had had more than ten surgeries – one every two to three years,’ said SEAS postdoctoral fellow Haritosh Patel. ‘We really wanted to understand why this was happening, and we realised that many of these obstructions and infections were tied to shunt designs that didn’t fully consider fluid dynamics as a fundamental part of their geometry. We noticed that the tubing geometry used in shunts closely resembles the kind of piping we rely on in household plumbing. While that simplicity has its advantages, we saw an opportunity to explore more creative, biomimetic solutions that better suit the complexity of the brain’s environment.’
Pursuing the problem from both a material and design perspective, the team quickly realised that there wasn’t a universally accepted fluid flow model for the brain ventricle space to guide them. ‘Okay, well, we can’t test our devices in a model, so why don’t we first make a better model?’ Patel said.
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The result is their computational tool, called BrainFlow, which combines detailed anatomical and physiological features of the brain to simulate the flow of cerebrospinal fluid flow in the presence of shunt implants. The model incorporates patient-specific medical imaging data along with pulse-induced flow to mimic a patient’s cerebrospinal fluid dynamics, all to offer insight into optimal shunt design and placement, and even choice of materials.
‘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 Joanna Aizenberg, the Amy Smith Berylson professor of materials science at SEAS and a professor of chemistry and chemical biology.
The Harvard team is currently conducting studies that use the model to test different designs of shunts and calculate their efficacy.
The research has been published in the Proceedings of the National Academy of Sciences.