Subgrouping of patients with rheumatoid arthritis based on pain, fatigue, inflammation, and psychosocial factors

Abstract

Objective: Among patients with rheumatoid arthritis (RA), pain may be attributed to peripheral inflammation or other causes, such as central pain mechanisms. The aim of this study was to use self-report measures and physical examination findings to identify clusters of RA patients who may have different causes of pain as well as different prognoses and treatment options.

Methods: Data from 169 RA patients with pain scores of >0 (on a 10-point numeric rating scale) in the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study were analyzed. The patients completed questionnaires on pain, fatigue, and psychosocial factors. A hierarchical agglomerative clustering procedure with Ward's method was used to obtain subgroups. Multivariate analysis of variance was used to determine the contribution of each variable in a cluster. General linear regression models were used to examine differences in clinical characteristics across subgroups. Discriminant analyses were performed to determine coefficients for linear combinations of variables that assigned cluster membership to individual cases.

Results: Three clusters best fit these data. Cluster 1 consisted of 89 individuals with low levels of inflammation, pain, fatigue, and psychosocial distress. Cluster 2 consisted of 57 individuals with minimal inflammation but high levels of pain, fatigue, and psychosocial distress. Cluster 3 consisted of 23 individuals with active inflammatory disease, manifested by high swollen joint counts, high C-reactive protein levels, and high levels of pain and fatigue.

Conclusion: Although most patients had low levels of inflammation, pain, and fatigue, 47.3% continued to report having moderate to high levels of pain and fatigue. Most of these patients had minimal signs of inflammation but high levels of fatigue, pain catastrophizing, and sleep disturbance, indicative of a chronic widespread pain syndrome.

Figure 1. Canonical discriminant functions used to differentiate clusters

Two discriminant functions significantly distinguished the clusters, accounting for 88.3% and 11.7% of the variance (P <0.0001). Three potential outliers (orange square) were noted.