PFI-TT: Sensor System for Early Warning of Hydrocephalus Shunt Failure

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The broader impact/commercial potential of this PFI project is to improve treatment of hydrocephalus. Since the 1950s, the gold standard of care has been to implant a tube, called a shunt, that drains excess fluid surrounding the brain elsewhere in the body where it can be safely absorbed. While this treatment is effective, the shunts fail at high rates and most commonly because of clogging. Because there are no clear patient symptoms and medical imaging technologies do not have sufficient resolution, patients live in fear that their headache may be an early sign of failure and must live close to a major medical center. A major reason why shunt technology has not advanced in many decades is that there are no sensors that can determine when and why shunts fail. In the near term, this innovation in miniaturized sensing technology will enable definitive diagnosis of shunt failure and in the long term, provide valuable data that can inform the design of failure-resistant shunts. The potential societal impacts are reduced emergency room visits and improved quality of life for patients and family members. The potential commercial impacts are new products for hydrocephalus and other medical conditions. The proposed project will enable a major improvement in hydrocephalus care since the introduction of shunts in the 1950s. Hydrocephalus patients suffer from an imbalance in the production of fluid surrounding the brain and spinal cord and its absorption by the body. A shunt implanted in the brain diverts fluid elsewhere in the body where it can be safely absorbed. The portion of the shunt implanted in the brain clogs at high rates leading to vague patient symptoms at first but that become progressively worse. A major obstacle is the inability to determine that a shunt is about to fail without surgery. This can be solved by integrating wireless sensors into the shunt that warn of failure. New advancements have enabled extremely small sensors that can monitor treatment directly in the shunt. The goal of this project is to integrate different sensor types that provide complementary information in a smart shunt, develop a simulated benchtop model of a hydrocephalic patient, and demonstrate that the smart shunt can provide accurate information on the proper function or failure of the shunt. The realistic benchtop model of the patient is critical to optimize sensors prior to future pre-clinical testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.