Multidisciplinary Approach to Spinal Cord Stimulation for Persistent Spinal Pain Syndromes: A 65-Month Integrated Data Collection From the Belgian Neuro-Pain® Real-World Data Register.

Spinal cord stimulation (SCS) serves as a treatment option for neuropathic pain conditions. Despite its widespread use and technological advancements over the last decade, the long-term efficacy of SCS remains a topic of debate. Consequently, there is an increasing demand for real-world, long-term data regarding its effectiveness. In 2018, the Belgian government launched a nationwide platform to monitor all SCS therapies. Five and a half years after its start, a full data extraction was conducted. In the present study, we update the findings of Bernaerts et al. (2024) from the 3-week trial period and the long-term follow-up of patients with persistent spinal pain syndrome and focus on the completion rates of the follow-ups and the battery lifetime of the implantable pulse generators (IPGs). Findings indicate that "yellow flags" or psychological variables can be confirmed as significant predictors of recovery and satisfaction following the trial. Additionally, these yellow flags were able to predict long-term disability. Analysis revealed that patients who completed the follow-up module displayed more active and less passive coping strategies for their pain, along with lower levels of illness anxiety prior to the trial's start, better physical and psychological functioning, and greater recovery and satisfaction with the trial's outcomes. However, adherence to the chronic follow-up module declined over time. Moreover, we investigated the battery life of both rechargeable and nonrechargeable batteries across various indication types. The real-world dataset indicated no significant differences in battery lifetime between rechargeable and nonrechargeable IPGs for each indication type. The long-term outcomes of neuromodulation are intricate and influenced by various factors. Data extracted from the Neuro-Pain® registry increasingly enable us to identify confounding factors and predictors of treatment success with greater precision. ClinicalTrials.gov identifier: NCT06835868.