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Cluster analysis of clinical data identifies fibromyalgia subgroups

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dc.contributor.author Docampo, Elisa
dc.contributor.author Collado, Antonio
dc.contributor.author Escaramís, Geòrgia
dc.contributor.author Carbonell, Jordi
dc.contributor.author Rivera, Javier
dc.contributor.author Vidal, Javier
dc.contributor.author Alegre, José
dc.contributor.author Rabionet, Raquel
dc.contributor.author Estivill, Xavier, 1955-
dc.date.accessioned 2023-12-18T07:00:16Z
dc.date.available 2023-12-18T07:00:16Z
dc.date.issued 2013
dc.identifier.citation Docampo E, Collado A, Escaramís G, Carbonell J, Rivera J, Vidal J, et al. Cluster analysis of clinical data identifies fibromyalgia subgroups. PLoS ONE. 2013 Sep 30;8(9):e74873. DOI: 10.1371/journal.pone.0074873
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10230/58558
dc.description.abstract Introduction. Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods. 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results. Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions. We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLoS ONE. 2013 Sep 30;8(9):e74873
dc.rights © 2013 Docampo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Fibromiàlgia
dc.subject.other Miàlgia
dc.subject.other Fatiga
dc.title Cluster analysis of clinical data identifies fibromyalgia subgroups
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0074873
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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