Subgrouping a large U.S. sample of patients with fibromyalgia using the fibromyalgia impact questionnaire-revised

Adrián Pérez-Aranda, Albert Feliu-Soler, Scott D. Mist, Kim D. Jones, Yolanda López-Del-hoyo, Rebeca Oliván-Arévalo, Anna Kratz, David A. Williams, Juan V. Luciano

Research output: Contribution to journalArticlepeer-review

Abstract

Fibromyalgia (FM) is a heterogeneous and complex syndrome; different studies have tried to describe subgroups of FM patients, and a 4-cluster classification based on the Fibromyalgia Impact Questionnaire-Revised (FIQR) has been recently validated. This study aims to cross-validate this classification in a large US sample of FM patients. A pooled sample of 6280 patients was used. First, we computed a hierarchical cluster analysis (HCA) using FIQR scores at item level. Then, a latent profile analysis (LPA) served to confirm the accuracy of the taxonomy. Additionally, a cluster calculator was developed to estimate the predicted subgroup using an ordinal regression analysis. Self-reported clinical measures were used to examine the external validity of the subgroups in part of the sample. The HCA yielded a 4-subgroup distribution, which was confirmed by the LPA. Each cluster represented a different level of severity: “Mild–moderate”, “moderate”, “moderate–severe”, and “severe”. Significant differences between clusters were observed in most of the clinical measures (e.g., fatigue, sleep problems, anxiety). Interestingly, lower levels of education were associated with higher FM severity. This study corroborates a 4-cluster distribution based on FIQR scores to classify US adults with FM. The classification may have relevant clinical implications for diagnosis and treatment response.

Original languageEnglish (US)
Article number247
Pages (from-to)1-11
Number of pages11
JournalInternational journal of environmental research and public health
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2021

Keywords

  • Clusters
  • Fibromyalgia
  • Fibromyalgia Impact Questionnaire Revised
  • Hierarchical cluster analysis
  • Latent profile analysis

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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