Clinical Decision Support System (CDSS) in Neurostimulation Therapy
Condition: Failed Back Surgery Syndrome
MeSH terms: Failed Back Surgery Syndrome
Study type: Interventional

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:

        1. -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.

        2. -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.

      fMR description: The MR experiment will be consistent with on-label requirements. The MR
      procedure will be performed prior to the device implantation to avoid bias. Even more, the
      Food and Drug Administration (FDA) does not recommend the examination of patients with this
      kind of devices for security reasons.

      Examinations will be performed in a 1'5 Tesla MR system (Philips Healthcare, Best, The
      Netherlands) at the Quiron Hospital. Decision of magnetic field is based on the quality of
      the examinations and must rely on label products and approval of company for interaction with
      implanted system.

      A head coil with 8 reception channels will be used. Once the patient has been positioned in
      the system, initial and fast localization images will be acquired in order to properly plan
      the MR sequences of the research study.

      After planning, a resting-state functional MR (rs-fMR) imaging sequence will be acquired,
      asking to the patient to be quiet with the eyes closed and thinking in a blue sky. The
      acquisition parameters will consist of an Echo Planar (EPI) dynamic T2* sequence, full brain
      coverage with the following parameters: TR=2000 ms; TE=30 ms; voxel size, 1.8 × 1.8 x 3.5 mm;
      flip angle, 90º; 40 axial slices; acquisition time 5:20 min.

      A DTI MR sequence will be acquired in order to analyze white matter microstructure and
      connectivity by tractography techniques with the following parameters: Spin-Echo Echo Planar
      Imaging (SE-EPI) sequence, single shot; full brain coverage; 64 gradient directions; b-value,
      1300 s/mm2; TR=6200 ms; TE=67 ms; voxel size, 2 x 2 x 2 mm; 60 axial slices; acquisition time
      9:40 min.

      An additional anatomic sequence will allow overlying structural and functional results and,
      in addition, obtaining the volumetry values of each brain region. The sequence parameters
      are: T1-weighted 3D gradient echo sequence (GRE), full brain coverage; TR=11.6 ms; TE=5.69;
      voxel size, 0.48 x 0.48 x 0.50 mm; flip angle, 8º; 280 axial slices; acquisition time 5:36
      min.

      After image acquisition, all data sets will be sent to the Imaging Biomarkers Platform of the
      Biomedical Imaging Research Group (GIBI230) of the La Fe Research Institute.

      The fMR images will be aligned in order to correct possible small patient's head movements
      during examination. For that, the open source SPM8 (Statistical Parametric Mapping,
      http://www.fil.ion.ucl.ac.uk/spm/) software tool will be used. After movement correction, a
      temporal correction will be applied optimizing the slice timing. Images will be then
      normalized to a standardized brain template in order to allow for the study of the
      oscillations between individuals. After such processes, data will be filtered by a3D-Gaussian
      kernel in order to increase signal-to-noise ratio (SNR) while minimizing inter-subject
      differences. Finally, the application of independent component analysis (ICA) algorithms will
      allow for the extraction of brain activation maps in the subject during the acquisition.

      The analysis of the DTI MR data for extracting white matter tracts connectivity will be
      performed with the open-source FSL software tool (http://www.fmrib.ox.ac.uk/fsl/). An initial
      Eddy currents correction will be applied to the images, in order to minimize slight images
      displacements and geometry distortions. The brain will be then segmented using the BET
      algorithm and brain data of all patients will be normalized to a common template for the
      group-based analysis. After this process, fractional anisotropy (FA), diffusivity (D) and
      orientation maps will be obtained .

      Once the MR images have been processed, the structural and functional connectivity properties
      have to be extracted from the regions of interest (ROI). The positioning of these regions
      will be obtained from the zones involved in the Default Mode Network (DMN). The DMN might
      take an important role in pain perception and shows a high correlation with the symptoms
      described by patients, which makes this network useful for the prediction of patient response
      after the implantation of electrical stimulation, either at a functional or at a structural
      level.

      Since images will be normalized to a common template, the automated area labeling AAL-tool
      will be used to define the regions of interest of the study, that compound the DMN and are
      formed by the medial temporal lobe, prefrontal cortex, posterior cingulate, precuneus and the
      parietal cortex. After measurement the connectivity parameters in these regions, a predictive
      model will be developed by combining clinical variables (scales and symptoms of each patient)
      and neuroimaging information. These models will be initially adjusted with a total of 30
      patients (training data) and later validated in a group of 30 patients (validation data),
      obtaining thereafter results of specificity, sensitivity and models precision.
    

Intervention name(s): SCS implanted with Precision SpectraTM
Intervention type(s): Device
Intervention description(s): Analyze the relationship between neuroimaging biomarkers and the different clinical scales and variables captured from each patient
Other intervention name(s): Functional Magnetic Resonance performed after implating SCS
Allocation: Non-Randomized
Intervention model: Sequential Assignment
Masking: None (Open Label)
Primary purpose: Screening