R.A. Coronado, PT, PhD, CSCS, FAAOMPT, Department of Physical Therapy, University of Florida, Box 100154, Gainesville, FL 32610-0154 (USA).
J.E. Bialosky, PT, PhD, OCS, FAAOMPT, Department of Physical Therapy, University of Florida.
Phys Ther. 2014 Aug;94(8):1111-22. doi: 10.2522/ptj.20130372. Epub 2014 Apr 24.
Cluster analysis can be used to identify individuals similar in profile based on response to multiple pain sensitivity measures. There are limited investigations into how empirically derived pain sensitivity subgroups influence clinical outcomes for individuals with spine pain.
The purposes of this study were: (1) to investigate empirically derived subgroups based on pressure and thermal pain sensitivity in individuals with spine pain and (2) to examine subgroup influence on 2-week clinical pain intensity and disability outcomes.
A secondary analysis of data from 2 randomized trials was conducted.
Baseline and 2-week outcome data from 157 participants with low back pain (n=110) and neck pain (n=47) were examined. Participants completed demographic, psychological, and clinical information and were assessed using pain sensitivity protocols, including pressure (suprathreshold pressure pain) and thermal pain sensitivity (thermal heat threshold and tolerance, suprathreshold heat pain, temporal summation). A hierarchical agglomerative cluster analysis was used to create subgroups based on pain sensitivity responses. Differences in data for baseline variables, clinical pain intensity, and disability were examined.
Three pain sensitivity cluster groups were derived: low pain sensitivity, high thermal static sensitivity, and high pressure and thermal dynamic sensitivity. There were differences in the proportion of individuals meeting a 30% change in pain intensity, where fewer individuals within the high pressure and thermal dynamic sensitivity group (adjusted odds ratio=0.3; 95% confidence interval=0.1, 0.8) achieved successful outcomes.
Only 2-week outcomes are reported.
Distinct pain sensitivity cluster groups for individuals with spine pain were identified, with the high pressure and thermal dynamic sensitivity group showing worse clinical outcome for pain intensity. Future studies should aim to confirm these findings.
聚类分析可用于根据对多种疼痛敏感测量的反应来识别具有相似特征的个体。关于经验衍生的疼痛敏感亚组如何影响脊柱疼痛患者的临床结果，研究有限。
本研究的目的是：（1）根据脊柱疼痛患者的压力和热痛觉敏感性，调查经验衍生的亚组；（2）研究亚组对 2 周临床疼痛强度和残疾结局的影响。
对 2 项随机试验的数据进行二次分析。
对 157 名腰痛（n=110）和颈痛（n=47）患者的基线和 2 周结局数据进行了检查。参与者完成了人口统计学、心理学和临床信息，并接受了疼痛敏感性测试方案的评估，包括压力（阈上压力痛觉）和热痛觉敏感性（热觉阈值和耐受力、阈上热痛觉、时间总和）。使用分层凝聚聚类分析根据疼痛敏感性反应创建亚组。检查了基线变量、临床疼痛强度和残疾数据的差异。
得出了 3 个疼痛敏感性聚类组：低疼痛敏感性、高热静敏性和高压力和热动敏性。在达到疼痛强度 30%变化的个体比例方面存在差异，其中高压力和热动敏性组（调整后的优势比=0.3；95%置信区间=0.1，0.8）的成功结果人数较少。
仅报告了 2 周的结果。
确定了脊柱疼痛患者存在不同的疼痛敏感性聚类组，其中高压力和热动敏性组的疼痛强度临床结局较差。未来的研究应旨在证实这些发现。
