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.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.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.doi http://dx.doi.org/10.1371/journal.pone.0074873
• dc.identifier.issn 1932-6203
• dc.identifier.uri http://hdl.handle.net/10230/58558
• 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.accessRights info:eu-repo/semantics/openAccess
• 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.type.version info:eu-repo/semantics/publishedVersion