Identification of symptom and functional domains that fibromyalgia patients would like to see improved: A cluster analysis

Robert (Rob) Bennett, Jon Russell, Joseph C. Cappelleri, Andrew G. Bushmakin, Gergana Zlateva, Alesia Sadosky

    Research output: Contribution to journalArticle

    26 Citations (Scopus)

    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 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). Conclusion. 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.

    Original languageEnglish (US)
    Article number134
    JournalBMC Musculoskeletal Disorders
    Volume11
    DOIs
    StatePublished - 2010

    Fingerprint

    Fibromyalgia
    Cluster Analysis
    Fatigue
    Pain
    Focus Groups
    Visual Analog Scale
    Therapeutics

    ASJC Scopus subject areas

    • Orthopedics and Sports Medicine
    • Rheumatology

    Cite this

    Identification of symptom and functional domains that fibromyalgia patients would like to see improved : A cluster analysis. / Bennett, Robert (Rob); Russell, Jon; Cappelleri, Joseph C.; Bushmakin, Andrew G.; Zlateva, Gergana; Sadosky, Alesia.

    In: BMC Musculoskeletal Disorders, Vol. 11, 134, 2010.

    Research output: Contribution to journalArticle

    Bennett, Robert (Rob) ; Russell, Jon ; Cappelleri, Joseph C. ; Bushmakin, Andrew G. ; Zlateva, Gergana ; Sadosky, Alesia. / Identification of symptom and functional domains that fibromyalgia patients would like to see improved : A cluster analysis. In: BMC Musculoskeletal Disorders. 2010 ; Vol. 11.
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    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 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). Conclusion. 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.",
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    AB - 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 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). Conclusion. 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.

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