Oregon Health & Science University, Portland, Oregon, USA.
BMC Musculoskelet Disord. 2010 Jun 28;11:134. doi: 10.1186/1471-2474-11-134.
The purpose of this study was to determine whether some of the clinical features of fibromyalgia (FM) that patients would like to see improved aggregate into definable clusters.
Seven hundred and eighty-eight patients with clinically confirmed FM and baseline pain > or =40 mm on a 100 mm visual analogue scale ranked 5 FM clinical features that the subjects would most like to see improved after treatment (one for each priority quintile) from a list of 20 developed during focus groups. For each subject, clinical features were transformed into vectors with rankings assigned values 1-5 (lowest to highest ranking). Logistic analysis was used to create a distance matrix and hierarchical cluster analysis was applied to identify cluster structure. The frequency of cluster selection was determined, and cluster importance was ranked using cluster scores derived from rankings of the clinical features. Multidimensional scaling was used to visualize and conceptualize cluster relationships.
Six clinical features clusters were identified and named based on their key characteristics. In order of selection frequency, the clusters were Pain (90%; 4 clinical features), Fatigue (89%; 4 clinical features), Domestic (42%; 4 clinical features), Impairment (29%; 3 functions), Affective (21%; 3 clinical features), and Social (9%; 2 functional). The "Pain Cluster" was ranked of greatest importance by 54% of subjects, followed by Fatigue, which was given the highest ranking by 28% of subjects. Multidimensional scaling mapped these clusters to two dimensions: Status (bounded by Physical and Emotional domains), and Setting (bounded by Individual and Group interactions).
Common clinical features of FM could be grouped into 6 clusters (Pain, Fatigue, Domestic, Impairment, Affective, and Social) based on patient perception of relevance to treatment. Furthermore, these 6 clusters could be charted in the 2 dimensions of Status and Setting, thus providing a unique perspective for interpretation of FM symptomatology.
本研究旨在确定纤维肌痛(FM)患者希望改善的一些临床特征是否可以聚合为可定义的聚类。
788 例临床确诊的 FM 患者,基线疼痛>或=40mm (100mm 视觉模拟评分),从焦点小组开发的 20 项清单中为每位患者排名前 5 位最希望在治疗后改善的 5 种 FM 临床特征(每个优先五分位数一个)。对于每个患者,将临床特征转换为具有排名分配值 1-5(从最低到最高排名)的向量。使用逻辑分析创建距离矩阵,并应用层次聚类分析识别聚类结构。确定聚类选择的频率,并使用源自临床特征排名的聚类得分对聚类重要性进行排名。多维标度用于可视化和概念化聚类关系。
根据关键特征确定并命名了 6 个临床特征聚类。按选择频率顺序,聚类分别为疼痛(90%;4 个临床特征)、疲劳(89%;4 个临床特征)、家务(42%;4 个临床特征)、功能障碍(29%;3 项功能)、情感(21%;3 个临床特征)和社会(9%;2 项功能)。54%的患者认为“疼痛聚类”最重要,其次是疲劳,28%的患者认为疲劳最重要。多维标度将这些聚类映射到两个维度:状态(由身体和情感领域界定)和环境(由个体和群体相互作用界定)。
根据患者对治疗相关性的看法,FM 的常见临床特征可以分为 6 个聚类(疼痛、疲劳、家务、功能障碍、情感和社会)。此外,这 6 个聚类可以在状态和环境的两个维度上绘制,从而为解释 FM 症状提供独特的视角。