Can Cerebrospinal Fluid Pressure Detect Catheter Complications in Patients Who Experience Loss of Effectiveness With Intrathecal Baclofen Therapy?

OBJECTIVE: The catheter status of patients who presented with loss of intrathecal baclofen (ITB) therapy effectiveness was investigated using measurements of cerebrospinal fluid (CSF) pressure transmitted through the catheter fluid path to the pump. The aim of the study was to estimate the appropriate threshold separating catheter complications from "normal" catheter function, and to compare catheter status based on CSF pressure with the clinical diagnosis. METHODS: This was a prospective, masked nonsignificant risk, research study. Patients (N = 47) received ITB for the treatment of severe spasticity and presented with symptoms of catheter malfunction. CSF pressure data were recorded using an external sensor connected to a needle inserted into the catheter access port. An algorithm calculated the energy of the variations in CSF pressure caused by respiration and heartbeat within the intrathecal space. These data were evaluated against a threshold that separated normal from abnormal catheter function. Catheter status based on the algorithm was compared with the clinical diagnosis. RESULTS: Complete data were available for 37 patients. Mean CSF pressure energy was significantly higher (p = 0.025; student t-test) for patients diagnosed with normal catheter function vs. catheters with complications. The CSF pressure algorithm matched the clinical diagnosis in 16 of 18 patients with catheter complications (sensitivity = 89%), and 13 of 19 patients with normal catheter function (specificity = 68%). CONCLUSION: In-clinic CSF pressure data acquisition is technically feasible. Overall, catheter status based on the algorithm demonstrated concordance with the clinical diagnosis in 29 of 37 patients (78.4%).

Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System.

Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.