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 and Juan V. Luciano
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Adrián Pérez-Aranda: Department of Basic, Evolutive and Educational Psychology, Faculty of Psychology, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Spain
Albert Feliu-Soler: Department of Basic, Evolutive and Educational Psychology, Faculty of Psychology, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallés, Spain
Scott D. Mist: Fibromyalgia Research and Treatment Group, Department of Anesthesiology & Perioperative Medicine, Oregon Health & Science University, Portland, OR 97239, USA
Kim D. Jones: School of Nursing, Linfield University and School of Medicine, Oregon Health & Science University, Portland, OR 97239, USA
Yolanda López-Del-Hoyo: Department of Psychology and Sociology, Univesity of Zaragoza, 50001 Zaragoza, Spain
Rebeca Oliván-Arévalo: AGORA Research Group, Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, 08830 St. Boi de Llobregat, Spain
Anna Kratz: Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI 48109, USA
David A. Williams: Departments of Anesthesiology, Internal Medicine, Psychiatry, and Psychology, University of Michigan, Ann Arbor, MI 48109, USA
Juan V. Luciano: AGORA Research Group, Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, 08830 St. Boi de Llobregat, Spain

IJERPH, 2020, vol. 18, issue 1, 1-11

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.

Keywords: Fibromyalgia Impact Questionnaire Revised; Fibromyalgia; clusters; latent profile analysis; hierarchical cluster analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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