IntroductionSpinal cord stimulation is a common treatment option for neuropathic pain conditions. Despite its extensive use and multiple technological evolutions, long term efficacy of spinal cord stimulation is debated. Most studies on spinal cord stimulation include a rather limited number of patients and/or follow-ups over a limited period. Therefore, there is an urgent need for real-world, long-term data.MethodsIn 2018, the Belgian government initiated a nationwide secure platform for the follow-up of all new and existing spinal cord stimulation therapies. This is a unique approach used worldwide. Four years after the start of centralized recording, the first global extraction of data was performed.ResultsHerein, we present the findings, detailing the different steps in the centralized procedure, as well as the observed patient and treatment characteristics. Furthermore, we identified dropouts during the screening process, the reasons behind discontinuation, and the evolution of key indicators during the trial period. In addition, we obtained the first insights into the evolution of the clinical impact of permanent implants on the overall functioning and quality of life of patients in the long-term.DiscussionAlthough these findings are the results of the first data extraction, some interesting conclusions can be drawn. The long-term outcomes of neuromodulation are complex and subject to many variables. Future data extraction will allow us to identify these confounding factors and the early predictors of success. In addition, we will propose further optimization of the current process.

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关于针对持续性脊柱疼痛综合征的脊髓刺激多学科方法的大规模真实世界数据：对 Neuro-Pain® 全国筛查和后续互动登记的首次评估

简介脊髓刺激是神经性疼痛的常见治疗选择。尽管其广泛使用和多种技术发展，脊髓刺激的长期疗效仍存在争议。大多数关于脊髓刺激的研究包括相当有限数量的患者和/或在有限时期内的随访。因此，迫切需要真实世界的长期数据。方法2018年，比利时政府启动了一个全国性的安全平台，用于跟踪所有新的和现有的脊髓刺激疗法。这是全世界使用的独特方法。集中记录开始四年后，首次进行了全球数据提取。结果在此，我们介绍了研究结果，详细说明了集中程序中的不同步骤，以及观察到的患者和治疗特征。此外，我们还确定了筛选过程中的退出情况、终止原因以及试验期间关键指标的变化。此外，我们首次深入了解永久种植体对患者整体功能和生活质量的长期临床影响的演变。讨论虽然这些发现是第一次数据提取的结果，但一些有趣的结论可以被画下来。神经调节的长期结果是复杂的并且受许多变量的影响。未来的数据提取将使我们能够识别这些混杂因素和成功的早期预测因素。此外，我们将提出对当前流程的进一步优化。