Advertisement
Clinical Research|Articles in Press

The Choice of Spinal Cord Stimulation vs Targeted Drug Delivery in the Management of Chronic Pain: Validation of an Outcomes Predictive Formula

      Abstract

      Objective

      In 2020, Mekhail et al published a formula that predicted the likelihood of a successful outcome for those who undergo spinal cord stimulation (SCS) for long-term pain management, based on retrospectively collected clinical and demographic data from one major medical center. The aim of this study is to validate such a predictive formula, prospectively, in a cohort of patients from multiple medical practices that are more representative of real-life clinical practice.

      Materials and Methods

      For the study, 939 patients who underwent successful SCS or targeted drug delivery (TDD) trials at multiple independent medical centers in the USA were enrolled into the Medtronic product surveillance registry data base before they underwent SCS or TDD device implantation, from 2018 to 2020. The registry data were collected prospectively but not specifically for this study. The data examined included demographic information, pain diagnosis, pain scores (visual analog scale [VAS]), Oswestry Disability Index scores, and quality-of-life scores at baseline and six months after implant. Because our goal is to validate the previously published predictive formula, in addition to the outcomes data previously mentioned, we collected the variables necessary for such a task: sex, age, depression, the presence of neuropathic pain, spine-related pain diagnosis, and persistent spinal pain syndrome “post laminectomy syndrome.” Spine-related pain diagnosis included subjects with chronic spine pain who never had back surgery and whose pain was not radicular nor neuropathic.

      Results

      Of 619 patients with SCS, 138 (22%) achieved ≥ 50% reductions of the baseline VAS at six months. The logistic model predicts SCS success with an area under the receiver operating characteristic curve (AUC) of 80% in the current validation data set. Of 320 patients with TDD, 147 (46%) achieved ≥ 50% reduction of the baseline VAS at six months. The logistic model predicts TDD success with an AUC of 78% in the current validation data set.

      Conclusion

      The study provides real life validation of the previously published predictive formula(4).

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Bhatia G.
        • Lau M.E.
        • Koury K.M.
        • Gulur P.
        Intrathecal Drug Delivery (ITDD) systems for cancer pain.
        F1000Res. 2013; 2: 96https://doi.org/10.12688/f1000research.2-96.v4
      1. Prager J, Deer T, Levy R, et al. Best practices for intrathecal drug delivery for pain. Neuromodulation. 2014;17:354–372 [discussion: 372]. https://doi.org/10.1111/ner.12146

        • Pope J.E.
        • Deer T.R.
        Intrathecal drug delivery for pain: a clinical guide and future directions.
        Pain Manag. 2015; 5: 175-183
        • Mekhail N.
        • Mehanny D.S.
        • Armanyous S.
        • et al.
        Choice of spinal cord stimulation versus targeted drug delivery in the management of chronic pain: a predictive formula for outcomes.
        Reg Anesth Pain Med. 2020; 45: 131-136
        • Altman D.G.
        • Vergouwe Y.
        • Royston P.
        • Moons K.G.
        Prognosis and prognostic research: validating a prognostic model.
        BMJ. 2009; 338: b605
        • Goudman L.
        • Van Buyten J.P.
        • De Smedt A.
        • et al.
        Predicting the response of high frequency spinal cord stimulation in patients with failed back surgery syndrome: a retrospective study with machine learning techniques.
        J Clin Med. 2020; 9: 4131
        • Bates D.
        • Schultheis B.C.
        • Hanes M.C.
        • et al.
        A comprehensive algorithm for management of neuropathic pain.
        Pain Med. 2019; 20: S2-S12
        • Kumsa D.
        • Steinke G.K.
        • Molnar G.F.
        • et al.
        Public regulatory databases as a source of insight for neuromodulation devices stimulation parameters.
        Neuromodulation. 2018; 21: 117-125
        • Pope J.E.
        • Deer T.R.
        • Falowski S.
        • et al.
        Multicenter retrospective study of neurostimulation with exit of therapy by explant.
        Neuromodulation. 2017; 20: 543-552
        • Van Buyten J.P.
        • Wille F.
        • Smet I.
        • et al.
        Therapy-related explants after spinal cord stimulation: results of an international retrospective chart review study.
        Neuromodulation. 2017; 20: 642-649
        • Al-Kaisy A.
        • Royds J.
        • Al-Kaisy O.
        • et al.
        Explant rates of electrical neuromodulation devices in 1177 patients in a single center over an 11-year period.
        Reg Anesth Pain Med. 2020; 45: 883-890
      2. Wang VC, Bounkousohn V, Fields K, Bernstein C, Paicius RM, Gilligan C. Explantation rates of high frequency spinal cord stimulation in two outpatient clinics. Neuromodulation. 2021;24:507–511. Published correction appears in Neuromodulation. 2021;24:1503.

        • Dougherty M.C.
        • Woodroffe R.W.
        • Wilson S.
        • Gillies G.T.
        • Howard M.A.
        • Carnahan R.M.
        Predictors of reduced opioid use ith spinal cord stimulation in patients ith chronic opioid use.
        Neuromodulation. 2020; 23: 126-132
        • Han J.L.
        • Murphy K.R.
        • Hussaini S.M.Q.
        • et al.
        Explantation rates and healthcare resource utilization in spinal cord stimulation.
        Neuromodulation. 2017; 20: 331-339
        • Gaskin D.
        • Richard P.
        The economic costs of pain in the United States.
        J Pain. 2012; 13: 715-724
        • Fine P.G.
        Long-term consequences of chronic pain: mounting evidence for pain as a neurological disease and parallels with other chronic disease states.
        Pain Med. 2011; 12: 996-1004
        • Kleinman N.
        • Patel A.A.
        • Benson C.
        • Macario A.
        • Kim M.
        • Biondi D.M.
        Economic burden of back and neck pain: effect of a neuropathic component.
        Popul Health Manag. 2014; 17: 224-232

      Comments