OMERACT-based fibromyalgia symptom subgroups: an exploratory cluster analysis

Abstract

IntroductionThe aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains.MethodsFemale patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical Outcomes Sleep Scale, the Multidimensional Fatigue Inventory, the Multiple Ability Self-Report Questionnaire, the Fibromyalgia Impact Questionnaire–Revised (FIQ-R) and the Short Form-36 between 1 June 2011 and 31 October 2011. Hierarchical agglomerative clustering was used to identify subgroups of patients with similar symptom profiles. To validate the results from this sample, hierarchical agglomerative clustering was repeated in an external sample of female patients with fibromyalgia with similar inclusion criteria.ResultsA total of 581 females with a mean age of 55.1 (range, 20.1 to 90.2) years were included. A four-cluster solution best fit the data, and each clustering variable differed significantly (P <0.0001) among the four clusters. The four clusters divided the sample into severity levels: Cluster 1 reflects the lowest average levels across all symptoms, and cluster 4 reflects the highest average levels. Clusters 2 and 3 capture moderate symptoms levels. Clusters 2 and 3 differed mainly in profiles of anxiety and depression, with Cluster 2 having lower levels of depression and anxiety than Cluster 3, despite higher levels of pain. The results of the cluster analysis of the external sample (n = 478) looked very similar to those found in the original cluster analysis, except for a slight difference in sleep problems. This was despite having patients in the validation sample who were significantly younger (P <0.0001) and had more severe symptoms (higher FIQ-R total scores (P = 0.0004)).ConclusionsIn our study, we incorporated core OMERACT symptom domains, which allowed for clustering based on a comprehensive symptom profile. Although our exploratory cluster solution needs confirmation in a longitudinal study, this approach could provide a rationale to support the study of individualized clinical evaluation and intervention.

Fibromyalgia: A Critical and Comprehensive Review

“…These were based on features of central sensitization alone [487], in combination with psychological distress and coping style [602], or combined with sleep disturbances and depressive symptoms [534]. Other approaches were founded on the core symptoms of FMS themselves [603] or symptom groups obtained via factor analysis [604,605] and in various combinations with comorbidities. Personality and FIQ scores have also formed the basis for cluster analyses [606,607].…”

Identification of clusters of individuals relevant to temporomandibular disorders and other chronic pain conditions

“…4,10,11,21,23,29,33–35,45,53,60,68,69,72 Among these, the Multidimensional Pain Inventory 33 (MPI) is probably the best known. The MPI emerged from biopsychosocial theory, and studies assessing outcomes based on MPI classification indicate that anatomically distinct disorders (headache, TMD, back pain) respond to treatments similarly within each cluster.…”

Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations

“…These would be a reasonable choice for phenotyping fatigue; the MFI in particular has been used in multiple pharmacologic treatment studies of patients with chronic pain [6;170]. Sleep disruption and fatigue often co-occur within symptom clusters in the context of a variety of persistent pain conditions [176;241], but to date, no published studies appear to have examined pre-treatment fatigue phenotypes as predictors of analgesic outcomes.…”