Moreton Bryan J, Tew Victoria, das Nair Roshan, Wheeler Maggie, Walsh David A, Lincoln Nadina B
University of Nottingham, Nottingham, UK.
Arthritis Care Res (Hoboken). 2015 Apr;67(4):519-28. doi: 10.1002/acr.22431.
Multiple mechanisms are involved in pain associated with osteoarthritis (OA). The painDETECT and Self-Report Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS) questionnaires screen for neuropathic pain and may also identify individuals with musculoskeletal pain who exhibit abnormal central pain processing. The aim of this cross-sectional study was to evaluate painDETECT and S-LANSS for classification agreement and fit to the Rasch model, and to explore their relationship to pain severity and pain mechanisms in OA.
A total of 192 patients with knee OA completed questionnaires covering different aspects of pain. Another group of 77 patients with knee OA completed questionnaires and underwent quantitative sensory testing for pressure-pain thresholds (PPTs). Agreement between painDETECT and S-LANSS was evaluated using kappa coefficients and receiver operator characteristic (ROC) curves. Rasch analysis of both questionnaires was conducted. Relationships between screening questionnaires and measures of pain severity or PPTs were calculated using correlations.
PainDETECT and S-LANSS shared a stronger correlation with each other than with measures of pain severity. ROC curves identified optimal cutoff scores for painDETECT and S-LANSS to maximize agreement, but the kappa coefficient was low (κ = 0.33-0.46). Rasch analysis supported the measurement properties of painDETECT but not those of S-LANSS. Higher painDETECT scores were associated with widespread reductions in PPTs.
The data suggest that painDETECT assesses pain quality associated with augmented central pain processing in patients with OA. Although developed as a screening questionnaire, painDETECT may also function as a measure of characteristics that indicate augmented central pain processing. Agreement between painDETECT and S-LANSS for pain classification was low, and it is currently unknown which tool may best predict treatment outcome.
骨关节炎（OA）相关疼痛涉及多种机制。疼痛检测问卷（painDETECT）和利兹神经病理性症状与体征自我报告评估问卷（S-LANSS）用于筛查神经病理性疼痛，也可识别存在异常中枢性疼痛处理的肌肉骨骼疼痛患者。本横断面研究的目的是评估painDETECT和S-LANSS在分类一致性和对拉施模型的拟合度方面的情况，并探讨它们与OA疼痛严重程度和疼痛机制的关系。
共有192例膝骨关节炎患者完成了涵盖疼痛不同方面的问卷。另一组77例膝骨关节炎患者完成问卷并接受压力痛阈值（PPT）的定量感觉测试。使用kappa系数和受试者工作特征（ROC）曲线评估painDETECT和S-LANSS之间的一致性。对两份问卷进行拉施分析。使用相关性计算筛查问卷与疼痛严重程度测量值或PPT之间的关系。
与疼痛严重程度测量值相比，painDETECT和S-LANSS之间的相关性更强。ROC曲线确定了painDETECT和S-LANSS的最佳截断分数以最大化一致性，但kappa系数较低（κ = 0.33 - 0.46）。拉施分析支持painDETECT的测量特性，但不支持S-LANSS的测量特性。较高的painDETECT分数与PPT的广泛降低相关。
数据表明，painDETECT评估了与OA患者中枢性疼痛处理增强相关的疼痛性质。尽管painDETECT是作为筛查问卷开发的，但它也可能作为一种指示中枢性疼痛处理增强特征的测量工具。painDETECT和S-LANSS在疼痛分类方面的一致性较低，目前尚不清楚哪种工具可能最能预测治疗结果。
