Identification and Validation of Clinically Relevant Clusters of Severe Fatigue in Rheumatoid Arthritis

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Abstract

Objective

The considerable heterogeneity of rheumatoid arthritis (RA)-related fatigue is the greatest challenge to determining pathogenesis. The identification of homogenous subtypes of severe fatigue would inform the design and analysis of experiments seeking to characterize the likely numerous causal pathways that underpin the symptom. This study aimed to identify and validate such fatigue subtypes in patients with RA.

Methods

Data were obtained from patients recruited to the British Society for Rheumatology Biologics register for RA, as either receiving traditional disease-modifying antirheumatic drugs (DMARD cohort, n = 522) or commencing anti-tumor necrosis factor therapy (anti-TNF cohort, n = 3909). In those reporting severe fatigue (Short-Form 36 vitality ≤ 12.5), this cross-sectional analysis applied hierarchical clustering with weighted-average linkage identified clusters of pain, fatigue, mental health (all Short-Form 36), disability (Health Assessment Questionnaire), and inflammation (erythrocyte sedimentation rate) in the DMARD cohort. K-means clustering sought to validate the solution in the anti-TNF cohort. Clusters were characterized using a priori generated symptom definitions and between-cluster comparisons.

Results

Four severe fatigue clusters, labeled as basic (46%), affective (40%), inflammatory (4.5%), and global (8.9%) were identified in the DMARD cohort. All clusters had severe levels of pain and disability and were distinguished by the presence/absence of poor mental health and high inflammation. The same symptom clusters were present in the anti-TNF cohort, although the proportion of participants in each cluster differed (basic = 28.7%; affective = 30.2%; global = 24.1%; inflammatory = 16.9%).

Conclusions

Among RA patients with severe fatigue, recruited to two diverse RA cohorts, clinically relevant clusters were identified and validated. These may provide the basis for future mechanistic studies and ultimately support a stratified approach to fatigue management.

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