title: Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Oregon Health & Science University, Portland, Oregon, USA. bennetrob1@comcast.net
authors.forename: Robert M
authors.initials: RM
authors.lastname: Bennett
authors.forename: Jon
authors.initials: J
authors.lastname: Russell
authors.forename: Joseph C
authors.initials: JC
authors.lastname: Cappelleri
authors.forename: Andrew G
authors.initials: AG
authors.lastname: Bushmakin
authors.forename: Gergana
authors.initials: G
authors.lastname: Zlateva
authors.forename: Alesia
authors.initials: A
authors.lastname: Sadosky
mesh_terms: D000203:Activities of Daily Living; D000328:Adult; D016000:Cluster Analysis; D015897:Comorbidity; D003627:Data Interpretation, Statistical; D004185:Disability Evaluation; D015673:Fatigue Syndrome, Chronic / Q000175:diagnosis / Q000453:epidemiology / Q000503:physiopathology; D005260:Female; D005356:Fibromyalgia / Q000175:diagnosis* / Q000453:epidemiology* / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D019964:Mood Disorders / Q000175:diagnosis / Q000453:epidemiology / Q000503:physiopathology; D010146:Pain / Q000175:diagnosis / Q000453:epidemiology / Q000503:physiopathology; D011788:Quality of Life / Q000523:psychology; D012647:Self-Assessment; D012655:Self-Evaluation Programs