Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298-0224 USA.
Phys Ther. 2020 Sep 28;100(10):1872-1881. doi: 10.1093/ptj/pzaa098.
The Western Ontario and McMaster Universities Osteoarthritis (WOMAC) pain scale quantifies knee pain severity with activities of daily living, but the potential impact of pain in other body regions on WOMAC pain scores has not been explored using a causal modeling approach. The purpose of this study was to determine if pain in other areas of the body impact WOMAC pain scores, a phenomenon referred to as "crosstalk."
Cross-sectional datasets were built from public use data available from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST). The WOMAC Pain Scale and generic hip, knee, ankle, foot and back pain measures were included. Three nested regression models grounded in causally based classical test theory determined the extent of crosstalk. Improvements in the coefficient of determination across the 3 models were used to determine the presence of crosstalk.
Causal modeling provided evidence of crosstalk in both OAI and MOST datasets. For example, in OAI, multiple statistical models demonstrated significant increases in coefficient of determination values (P < .0001) as additional pain areas were added to the models.
Crosstalk appears to be a clinically important source of error in the WOMAC Pain Scale, particularly for patients with a larger number of painful body regions and when contralateral knee joint pain is more severe.
This study has important implications for arthritis research. It also should raise clinician awareness of the threat to score interpretation and the need to consider the extent of pain in other body regions when interpreting WOMAC pain scores.
安大略西部和麦克马斯特大学骨关节炎(WOMAC)疼痛量表通过日常生活活动量化膝关节疼痛的严重程度,但尚未通过因果建模方法探讨身体其他部位疼痛对 WOMAC 疼痛评分的潜在影响。本研究旨在确定身体其他部位的疼痛是否会影响 WOMAC 疼痛评分,这种现象称为“串扰”。
从骨性关节炎倡议(OAI)和多中心骨关节炎研究(MOST)公开可用的数据中构建了横断面数据集。包括 WOMAC 疼痛量表和通用髋关节、膝关节、踝关节、足部和背部疼痛测量值。基于因果经典测试理论的三个嵌套回归模型确定了串扰的程度。通过 3 个模型的决定系数的改进来确定串扰的存在。
因果模型为 OAI 和 MOST 数据集中的串扰提供了证据。例如,在 OAI 中,多个统计模型表明,随着模型中添加更多疼痛区域,决定系数值显著增加(P<.0001)。
串扰似乎是 WOMAC 疼痛量表中一个重要的临床误差源,尤其是对于疼痛区域较多的患者,以及当对侧膝关节疼痛更严重时。
本研究对关节炎研究具有重要意义。它还应提高临床医生对评分解释威胁的认识,以及在解释 WOMAC 疼痛评分时需要考虑身体其他部位疼痛的程度。