Clinical Decision Support System (CDSS) in Neurostimulation Therapy (CDSS)

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

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.

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. 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.

30 patients with chronic pain (at least 6 months) with pre- and post-surgery evaluation (imaging and clinical evaluation) implanted with Precision SpectraTM for the validation study and for the construction of the predictive model.

20 patients with chronic pain (at least 6 months) with degenerative spine pain. Non-specific low-back pain, nociceptive pain / mixed neuropathic. This will be the comparator group

10 volunteers without pain or related disease, age less than 25 years, to establish a control group whose pattern is used as a comparator with chronic pain groups. This is the control group

Analyze the relationship between neuroimaging biomarkers and the different clinical scales and variables captured from each patient

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.

to describe neuronal circuits involved in chronic pain by comparation between patients and control subjects; identify differences in the neural circuits between patients who have successfully undergone the implantation of the SCS and who failed the trial phase attending to the current criteria

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.

Simpson EL, Duenas A, Holmes MW, Papaioannou D, Chilcott J. Spinal cord stimulation for chronic pain of neuropathic or ischaemic origin: systematic review and economic evaluation. Health Technol Assess. 2009 Mar;13(17):iii, ix-x, 1-154. doi: 10.3310/hta13170.

Turner JA, Loeser JD, Deyo RA, Sanders SB. Spinal cord stimulation for patients with failed back surgery syndrome or complex regional pain syndrome: a systematic review of effectiveness and complications. Pain. 2004 Mar;108(1-2):137-47. doi: 10.1016/j.pain.2003.12.016.

Borsook D, Sava S, Becerra L. The pain imaging revolution: advancing pain into the 21st century. Neuroscientist. 2010 Apr;16(2):171-85. doi: 10.1177/1073858409349902.

Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001 Jan 16;98(2):676-82. doi: 10.1073/pnas.98.2.676.

Taylor KS, Seminowicz DA, Davis KD. Two systems of resting state connectivity between the insula and cingulate cortex. Hum Brain Mapp. 2009 Sep;30(9):2731-45. doi: 10.1002/hbm.20705.

Hodaie M, Chen DQ, Quan J, Laperriere N. Tractography delineates microstructural changes in the trigeminal nerve after focal radiosurgery for trigeminal neuralgia. PLoS One. 2012;7(3):e32745. doi: 10.1371/journal.pone.0032745. Epub 2012 Mar 6.

Leal PRL, Roch JA, Hermier M, Souza MAN, Cristino-Filho G, Sindou M. Structural abnormalities of the trigeminal root revealed by diffusion tensor imaging in patients with trigeminal neuralgia caused by neurovascular compression: a prospective, double-blind, controlled study. Pain. 2011 Oct;152(10):2357-2364. doi: 10.1016/j.pain.2011.06.029. Epub 2011 Aug 10.

Lutz J, Jager L, de Quervain D, Krauseneck T, Padberg F, Wichnalek M, Beyer A, Stahl R, Zirngibl B, Morhard D, Reiser M, Schelling G. White and gray matter abnormalities in the brain of patients with fibromyalgia: a diffusion-tensor and volumetric imaging study. Arthritis Rheum. 2008 Dec;58(12):3960-9. doi: 10.1002/art.24070.

Ruscheweyh R, Deppe M, Lohmann H, Stehling C, Floel A, Ringelstein BE, Knecht S. Pain is associated with regional grey matter reduction in the general population. Pain. 2011 Apr;152(4):904-911. doi: 10.1016/j.pain.2011.01.013. Epub 2011 Feb 5.

Gustin SM, Peck CC, Wilcox SL, Nash PG, Murray GM, Henderson LA. Different pain, different brain: thalamic anatomy in neuropathic and non-neuropathic chronic pain syndromes. J Neurosci. 2011 Apr 20;31(16):5956-64. doi: 10.1523/JNEUROSCI.5980-10.2011.

Deer TR, Grider JS, Lamer TJ, Pope JE, Falowski S, Hunter CW, Provenzano DA, Slavin KV, Russo M, Carayannopoulos A, Shah JM, Harned ME, Hagedorn JM, Bolash RB, Arle JE, Kapural L, Amirdelfan K, Jain S, Liem L, Carlson JD, Malinowski MN, Bendel M, Yang A, Aiyer R, Valimahomed A, Antony A, Craig J, Fishman MA, Al-Kaisy AA, Christelis N, Rosenquist RW, Levy RM, Mekhail N. A Systematic Literature Review of Spine Neurostimulation Therapies for the Treatment of Pain. Pain Med. 2020 Nov 7;21(7):1421-1432. doi: 10.1093/pm/pnz353. Erratum In: Pain Med. 2021 Feb 4;22(1):236.

De Andres J, Navarrete-Rueda F, Fabregat G, Garcia-Gutierrez MS, Monsalve-Dolz V, Harutyunyan A, Minguez-Marti A, Rodriguez-Lopez R, Manzanares J. Differences in Gene Expression of Endogenous Opioid Peptide Precursor, Cannabinoid 1 and 2 Receptors and Interleukin Beta in Peripheral Blood Mononuclear Cells of Patients With Refractory Failed Back Surgery Syndrome Treated With Spinal Cord Stimulation: Markers of Therapeutic Outcomes? Neuromodulation. 2021 Jan;24(1):49-60. doi: 10.1111/ner.13111. Epub 2020 Feb 6.

Dworkin RH, O'Connor AB, Kent J, Mackey SC, Raja SN, Stacey BR, Levy RM, Backonja M, Baron R, Harke H, Loeser JD, Treede RD, Turk DC, Wells CD. Interventional management of neuropathic pain: NeuPSIG recommendations. Pain. 2013 Nov;154(11):2249-2261. doi: 10.1016/j.pain.2013.06.004. Epub 2013 Jun 6.

De Andres J, Monsalve-Dolz V, Fabregat-Cid G, Villanueva-Perez V, Harutyunyan A, Asensio-Samper JM, Sanchis-Lopez N. Prospective, Randomized Blind Effect-on-Outcome Study of Conventional vs High-Frequency Spinal Cord Stimulation in Patients with Pain and Disability Due to Failed Back Surgery Syndrome. Pain Med. 2017 Dec 1;18(12):2401-2421. doi: 10.1093/pm/pnx241.

Oakley JC, Krames ES, Stamatos J, Foster AM. Successful long-term outcomes of spinal cord stimulation despite limited pain relief during temporary trialing. Neuromodulation. 2008 Jan;11(1):66-73. doi: 10.1111/j.1525-1403.2007.00145.x.

North RB, Calodney A, Bolash R, Slavin KV, Creamer M, Rauck R, Vahedifar P, Fox I, Ozaktay C, Panchal S, Vanquathem N. Redefining Spinal Cord Stimulation "Trials": A Randomized Controlled Trial Using Single-Stage Wireless Permanent Implantable Devices. Neuromodulation. 2020 Jan;23(1):96-101. doi: 10.1111/ner.12970. Epub 2019 Jun 3.

Eldabe S, Gulve A, Thomson S, Baranidharan G, Duarte R, Jowett S, Sandhu H, Chadwick R, Brookes M, Tariq A, Earle J, Bell J, Kansal A, Rhodes S, Taylor RS. Does a Screening Trial for Spinal Cord Stimulation in Patients with Chronic Pain of Neuropathic Origin have Clinical Utility and Cost-Effectiveness? (TRIAL-STIM Study): study protocol for a randomised controlled trial. Trials. 2018 Nov 16;19(1):633. doi: 10.1186/s13063-018-2993-9. Erratum In: Trials. 2019 Oct 28;20(1):610.

Patel SK, Gozal YM, Saleh MS, Gibson JL, Karsy M, Mandybur GT. Spinal cord stimulation failure: evaluation of factors underlying hardware explantation. J Neurosurg Spine. 2019 Oct 4:1-6. doi: 10.3171/2019.6.SPINE181099. Online ahead of print.

Pahapill PA, Chen G, Arocho-Quinones EV, Nencka AS, Li SJ. Functional connectivity and structural analysis of trial spinal cord stimulation responders in failed back surgery syndrome. PLoS One. 2020 Feb 19;15(2):e0228306. doi: 10.1371/journal.pone.0228306. eCollection 2020.

Malfliet A, Coppieters I, Van Wilgen P, Kregel J, De Pauw R, Dolphens M, Ickmans K. Brain changes associated with cognitive and emotional factors in chronic pain: A systematic review. Eur J Pain. 2017 May;21(5):769-786. doi: 10.1002/ejp.1003. Epub 2017 Feb 1.

Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, Gramfort A, Thirion B, Varoquaux G. Machine learning for neuroimaging with scikit-learn. Front Neuroinform. 2014 Feb 21;8:14. doi: 10.3389/fninf.2014.00014. eCollection 2014.

Deogaonkar M, Sharma M, Oluigbo C, Nielson DM, Yang X, Vera-Portocarrero L, Molnar GF, Abduljalil A, Sederberg PB, Knopp M, Rezai AR. Spinal Cord Stimulation (SCS) and Functional Magnetic Resonance Imaging (fMRI): Modulation of Cortical Connectivity With Therapeutic SCS. Neuromodulation. 2016 Feb;19(2):142-53. doi: 10.1111/ner.12346. Epub 2015 Sep 16.

Amirdelfan K, Webster L, Poree L, Sukul V, McRoberts P. Treatment Options for Failed Back Surgery Syndrome Patients With Refractory Chronic Pain: An Evidence Based Approach. Spine (Phila Pa 1976). 2017 Jul 15;42 Suppl 14:S41-S52. doi: 10.1097/BRS.0000000000002217.

Sivanesan E, Maher DP, Raja SN, Linderoth B, Guan Y. Supraspinal Mechanisms of Spinal Cord Stimulation for Modulation of Pain: Five Decades of Research and Prospects for the Future. Anesthesiology. 2019 Apr;130(4):651-665. doi: 10.1097/ALN.0000000000002353.

Bentley LD, Duarte RV, Furlong PL, Ashford RL, Raphael JH. Brain activity modifications following spinal cord stimulation for chronic neuropathic pain: A systematic review. Eur J Pain. 2016 Apr;20(4):499-511. doi: 10.1002/ejp.782. Epub 2015 Oct 1.

Kolesar TA, Bilevicius E, Kornelsen J. Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome. Scand J Pain. 2017 Jul;16:10-14. doi: 10.1016/j.sjpain.2017.01.008. Epub 2017 Feb 20.

De Groote S, Goudman L, Peeters R, Linderoth B, Van Schuerbeek P, Sunaert S, De Jaeger M, De Smedt A, De Andres J, Moens M. The influence of High Dose Spinal Cord Stimulation on the descending pain modulatory system in patients with failed back surgery syndrome. Neuroimage Clin. 2019;24:102087. doi: 10.1016/j.nicl.2019.102087. Epub 2019 Nov 12.

Yu CX, Ji TT, Song H, Li B, Han Q, Li L, Zhuo ZZ. Abnormality of spontaneous brain activities in patients with chronic neck and shoulder pain: A resting-state fMRI study. J Int Med Res. 2017 Feb;45(1):182-192. doi: 10.1177/0300060516679345. Epub 2017 Jan 25.

De Groote S, Goudman L, Peeters R, Linderoth B, Vanschuerbeek P, Sunaert S, De Jaeger M, De Smedt A, Moens M. Magnetic Resonance Imaging Exploration of the Human Brain During 10 kHz Spinal Cord Stimulation for Failed Back Surgery Syndrome: A Resting State Functional Magnetic Resonance Imaging Study. Neuromodulation. 2020 Jan;23(1):46-55. doi: 10.1111/ner.12954. Epub 2019 Apr 11.