Noninvasive, wireless thermal sensors for the quantitative monitoring of ventricular shunt function in patients with hydrocephalus

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ABSTRACT Hydrocephalus is a neurological disorder caused by buildup of excess cerebrospinal fluid (CSF) in the brain, and leads to lethargy, seizures, coma, or death. It is treated with the surgical implantation of a catheter, known as a ventricular shunt, that diverts excess CSF away from the brain and into a distal absorptive site, such as the abdomen. Unfortunately, due to occlusion or mispositioning, shunts have extremely high failure rates - 51% in 2 years, and 98% over 10 years - and often require corrective surgical procedures, known as revisions. 125,000 shunt implantations or revisions are performed annually in the United States, at a cost of $2 billion. Diagnosing shunt failure is extremely difficult, due the non-specific nature of its symptoms. Imaging tests such as CT are commonly used despite their long-term radiation exposure risks and invasive tests such as CSF aspiration and radionuclide tracing are painful and carry significant infection risks. All the above tests have poor diagnostic performance, with low sensitivities (~80%) and specificities (~55%), in large part because they do not directly measure flow dynamics, arguably the most important and relevant shunt performance metric. The continuous, non-invasive, real-time monitoring of CSF flow through implanted shunts represents a critical unmet need. Clinicians would use these data for point-of-care diagnostics, and researchers would use them to better understand shunt failure, and elucidate the pathophysiology of poorly-understood conditions such as normal-pressure hydrocephalus. The present proposal addresses this need by capitalizing on existing wireless sensor hardware to develop the first skin mounted wireless product to give quantitative CSF flow data in implanted shunts. Using precise measurements of thermal transport, we will develop novel algorithms to characterize and quantify flow through underlying shunts with high accuracy. Preliminary patient data, benchtop tests, and computer modeling confirm the ability of the sensor to produce high-quality flow data while being mechanically unobtrusive to the patient. Phase I activities will validate the sensor on large animal models, develop algorithms to deliver quantitative flow data, and implement a quality management system to govern software development, ultimately leading to IRB approval for clinical testing. Phase II activities will validate diagnostic value of the sensor’s performance clinically through a human trial, ultimately leading to a marketing submission to the FDA.