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BMC Musculoskeletal Disorders

, 11:134

First Online: 28 June 2010Received: 10 October 2009Accepted: 28 June 2010


BackgroundThe 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.

MethodsSeven hundred and eighty-eight patients with clinically confirmed FM and baseline pain ≥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.

ResultsSix 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.

ConclusionCommon 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.


VASVisual analog scale

SASStatistical Analysis Software.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2474-11-134 contains supplementary material, which is available to authorized users.

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Autor: Robert M Bennett - Jon Russell - Joseph C Cappelleri - Andrew G Bushmakin - Gergana Zlateva - Alesia Sadosky


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