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Individual patient disease activity expression profiles modify the relation between disease activity and structural
Chiara Ripepi1, Bram van Es1, Janneke Tekstra1
1Rheumatology and Clinical Immunology, University Medical Center Utrecht, Utrecht, The Netherlands.
Objectives:
Disease activity in rheumatoid arthritis (RA) is commonly assessed using composite indices such as the Disease Activity Score in 28 Joints (DAS28). However, individual disease activity markers can be more or less pronounced in patient subgroups, and markers beyond disease activity indices may contribute to relevant disease activity patterns. This study aimed to identify patient subgroups with 'disease activity expression profiles' and evaluate their relevance.
Methods:
Routine-care data at University Medical Center Utrecht were extracted. Seventeen parameters associated with disease activity were included in a clustering analysis, with the optimal number of clusters determined using fit statistics and clinical interpretation. Using random effects modelling, the association between DAS28 and radiographic progression was assessed, including whether this relation was modified by expression profile.
Results:
Among 1167 patients with RA, three disease activity expression profiles were identified: (i) Systemic inflammatory (n = 330), characterised by high swollen joint counts and elevated inflammatory markers; (ii) Symptom dominant (n = 191), marked by high patient-reported activity and tender-swollen joint discrepancy; and (iii) Low-typical (n = 646), with low-to-moderate disease activity and less divergent marker expression. Higher DAS28 was associated with radiographic progression (odds ratio = 1.40 [1.03-1.92], P = .032). This relation was confined to the Low-typical and Systemic inflammatory subgroups, whereas absent in the Symptom-dominant group (P interaction = .015). Patients in the Systemic inflammatory subgroup had a higher progression risk.
Conclusions:
Derived disease activity expression profiles influence the predictive value of DAS28. Taking identified expression profiles into account may improve disease activity interpretation and support tailored monitoring and management in RA.
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