Development of a Predictive Model of Effectiveness for the Implantation of Electrical Neurostimulators in Patients With Chronic Pain Using Imaging Biomarkers Extracted From Magnetic Resonance
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 60
- Primary Endpoint
- Clinical decision support system (CDSS) for selection of patients candidates for SCS implant
Study Overview
Brief Summary
Chronic pain is correlated with alterations in the structure and function of the brain, developed according to the phenotype of pain. Still today, the data on functional connectivity (FC), on chronic back pain, in patients with failed back surgery syndrome (FBSS), is limited. The selection process for the ideal candidate for spinal cord stimulation (SCS) is based on results from test and functional variables analysis as well as pain evaluation. In addition to the difficulties in the initial selection of patients and the predictive analysis of the test phase, which undoubtedly impact on the results in the middle and long term, the rate of explants is one of the most important concerns, in the analysis of suitability of implanted candidates. The hypothesis is that the structural and functional quantitative information provided by imaging biomarkers will improve the characterization of the patients compared to the characterization with the current clinical variables alone and this will allow establishing a CDSS that improve the effectiveness of the SCS implantation, optimizing human, economic and psychological resources.
A prospective, consecutive and observational, open-label, single-center study conducted at the Multidisciplinary Pain Management Department of our University Hospital. A total of 69 subjects were initially included in the study. The population split in 3 groups:
- Interventional Group-SCS, included 35 patients with failed back surgery syndrome (FBSS) who were treated with SCS implants.
- Comparator group included 23 patients with patients with chronic low-back pain who were treated with conventional medication (CM) for their pain.
- Control Group included 11 subjects as health controls who volunteered to participate in the study.
MR images were obtained in a 1.5T MR system (Ingenia, Philips, Best, The Netherlands) using an 8-channel head coil.Clinical variables were evaluated at two different time points baseline and 12 months after SCS implantation or conventional medication. An ad hoc database was created to evaluate the different variables involved in pain , including sociodemographic variables (age, gender, level of studies and marital status), clinical variables (anxiety, depression, sleeping hours, resilience, NRS, the Pain Detect Questionnaire (PD-Q)) , and the images obtained from the fMRI.
Detailed Description
Primary objective:To develop a predictive model integrated in a clinical decision support system (CDSS) feed by neuroimaging quantitative information objectively extracted from Magnetic Resonance (MR) images, which maximizes the appropriate use and effectiveness of electrical stimulation devices surgically implanted in selected patients with chronic pain.
Exploratory Objectives:
- -Analyze functional and anatomical brain connectivity patterns in patients with chronic pain , to develop a predictive model based on quantitative magnetic resonance neuroimaging which maximizes the effectiveness of neurostimulation devices surgically implanted in patients with chronic pain.
- -Analyze the relationship between neuroimaging biomarkers and the different clinical scales and variables captured from each patient (VAS, Oswestry Disability Index, DN4, Pain Detect, Moss, SF12, coping scale, optimism, resilience and HAD).
Test Device:1.5 Tesla MR system (Philips Healthcare, Best, The Netherlands) Boston Scientific Neuromodulation (BSN) Precision Spectra™ Spinal Cord Stimulation System with Illumina 3D™ Software and 32 contacts.
Device Description: Precision Spectra™ system IPG is a multiple independent current controlled pulse generator, capable of delivering current through 32 contacts. It is powered by a 3D programming software that considers the anatomical position of the leads. Two models of SCS leads will be provided, featuring 8 or 16 contacts with 1.3 mm diameter, 3 mm contact length, and contact spacing of 1, 4 or 6 mm. The use of SCS extensions will be optional to connect the IPG.
Study Design
- Study Type
- Interventional
- Allocation
- Non Randomized
- Intervention Model
- Sequential
- Primary Purpose
- Screening
- Masking
- None
Eligibility Criteria
- Ages
- 20 Years to 80 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •Patients presenting pain of more than 6 months in duration
- •VAS Score at baseline ≥ 5
- •Patients with degenerative spine pain. Non specific low-back pain, nociceptive pain / mixed neuropathic
- •Post-operative spine pain, failed back surgery syndrome, mixed pain
- •Low consumption of analgesic and adjuvant drugs.
- •Pure radiculopathy
- •No suffering other serious chronic diseases.
- •No history of drug or alcohol.
Exclusion Criteria
- •Having implanted pacemakers, stimulators or hearing aids incompatible with MR imaging.
- •Patients presenting psychiatric illness or significant cognitive deficits.
- •Psychological instability.
- •History of alcohol and drugs.
- •Severe coagulopathy.
- •Pending Surgery.
Outcomes
Primary Outcomes
Clinical decision support system (CDSS) for selection of patients candidates for SCS implant
Time Frame: 12 months
to analyze the neuronal circuits involved in FBSS patients in order to extract predictive imaging biomarkers capable of determining the characteristics of patients that predict the success of SCS implants. This information might be used to develop a CDSS to maximize the effectiveness of electrical stimulation devices surgically implanted in patients with chronic pain.
Secondary Outcomes
- neuronal circuits involved in chronic pain(12 months)
Investigators
Jose De Andres
Tenured Professor of Anesthesiology, Valencia University Medical School. Chairman of the Department of Anesthesiology Critical Care and Pain Management. General University Hospital. Valencia (Spain)
General University Hospital of Valencia