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Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis
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 10...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908076/ https://www.ncbi.nlm.nih.gov/pubmed/20584327 http://dx.doi.org/10.1186/1471-2474-11-134 |
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author | Bennett, Robert M Russell, Jon Cappelleri, Joseph C Bushmakin, Andrew G Zlateva, Gergana Sadosky, Alesia |
author_facet | Bennett, Robert M Russell, Jon Cappelleri, Joseph C Bushmakin, Andrew G Zlateva, Gergana Sadosky, Alesia |
author_sort | Bennett, Robert M |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-2908076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29080762010-07-22 Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis Bennett, Robert M Russell, Jon Cappelleri, Joseph C Bushmakin, Andrew G Zlateva, Gergana Sadosky, Alesia BMC Musculoskelet Disord Research Article 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. BioMed Central 2010-06-28 /pmc/articles/PMC2908076/ /pubmed/20584327 http://dx.doi.org/10.1186/1471-2474-11-134 Text en Copyright ©2010 Bennett et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Bennett, Robert M 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 |
title | Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
title_full | Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
title_fullStr | Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
title_full_unstemmed | Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
title_short | Identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
title_sort | identification of symptom and functional domains that fibromyalgia patients would like to see improved: a cluster analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908076/ https://www.ncbi.nlm.nih.gov/pubmed/20584327 http://dx.doi.org/10.1186/1471-2474-11-134 |
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