Pain Catastrophizing Does Not Predict Spinal Cord Stimulation Outcomes: A Cohort Study of 259 Patients With Long-Term Follow-Up

Neuromodulation: Technology at the Neural Interface(2021)

引用 18|浏览0
暂无评分
摘要
Objective Spinal cord stimulation (SCS) is an important treatment modality used to treat chronic neuropathic pain. However, reported success rates of 26%-70% entail an increased focus on patient selection. An area of core interest is psychological evaluation, often using scales such as the Pain Catastrophizing Scale (PCS). The aim of this study was to assess the relation between baseline PCS scores obtained before implantation and SCS outcomes defined as (1) Rating on Patients’ Global Impression of Change scale (PGIC), (2) Pain relief on the Numeric Rating Scale (NRS), (3) Cessation of pain medication, and (4) Risk of permanent explantation. Materials and Methods Using records from the Neurizon Neuromodulation Database, we performed a multicenter open cohort study of 259 permanently implanted SCS patients. Follow-up ranged from six months to nine years (median = three years). For each of the defined SCS outcomes, patients were grouped according to their latest follow-up registration. Subsequently, we used a one-way ANOVA and exact t-tests to compare mean baseline PCS scores between groups. Results No difference in mean baseline PCS scores was found between PGIC groups. Baseline PCS scores was not associated with the probability of obtaining 30% or 50% pain relief on latest registration. Baseline PCS scores of patients able to cease all usage of tricyclic antidepressants, antiepileptics, or opioids during SCS treatment did not differ from baseline scores of continuous users. We found no association between baseline PCS scores and risk of permanent explantation. Conclusion This study did not demonstrate any associations between baseline PCS scores and SCS outcomes.
更多
查看译文
关键词
Spinal cord stimulation,outcome predictors,psychological evaluation,catastrophizing,registries
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要