This project aims at conducting the basic research necessary for the subsequent development of a smart cerebrospinal fluid drainage system for normal pressure hydrocephalus that addresses the key shortcomings of current drainage technology: infections, poor cerebrospinal fluid flow control and mechanical failure.

The term “normal pressure hydrocephalus” (NPH) describes a clinical entity consisting of the triad of walking disturbance, memory loss and urine incontinence, coupled with the findings of normal cerebrospinal fluid (CSF) pressure and enlarged brain ventricles. The preferred treatment for NPH is the continuous drainage of CSF from the brain ventricular space to the patient’s peritoneal space through an assembly of implanted catheters and a one-way differential pressure valve against overdrainage and CSF reflux. This assembly is referred to as “CSF shunt”. The mechanical failure rate of these CSF shunts is up to 40% within the first year of implantation and 4 to 5% in the subsequent years. The shunt infection rate is about 5%. As a consequence, roughly half of the treated patients require follow-up surgery within the first two years. Current shunts rely on differential-pressure valves to control CSF drainage. In most of them, the opening pressure cannot be changed after implantation. Patients with this standard type of valve often suffer from the symptoms of CSF over- or underdrainage.

We have developed a novel shunt concept that addresses all of the above problems. The envisioned shunt regulates CSF drainage using a smart control system, which analyzes the patient’s intracranial dynamics to determine the required outflow rate. Exposure of the cerebrospinal fluid to the surfaces of the shunt is minimized through an innovative design, thereby minimizing the adhesion area for pathogens and reducing the potential for infections. This design also reduces the number of moving parts that are in contact with the CSF, thus limiting the risk of mechanical failure.

Within this project, we will develop a computational model of the CSF spaces based on magnetic resonance imaging (MRI) data. This model will allow us to investigate the optimal control algorithm for the envisioned shunt. We will clinically investigate the shunt infection and surgical implantation process in order to optimize our innovative shunt design with respect to infections and mechanical failure. We will, finally, build a demonstrator and testing system, from which the envisioned shunt can be developed.