Early predictive factors in routine clinical practice for rituximab therapy response in patients with rheumatoid arthritis.

Introduction: Identifying early predictive factors of how rheumatoid arthritis (RA) patients respond to rituximab (RTX) treatment is crucial for both individual treatment outcome and the improvement of clinical practice overall. This study aimed to identify early predictive factors available in standard clinical practice for predicting RTX treatment outcomes in RA patients.

Material and methods: Data on seventy patients diagnosed with RA treated with RTX (two 1,000 mg doses 2 weeks apart or two 500 mg doses 2 weeks apart) were retrospectively collected. Baseline information collected at the initiation of RTX treatment included patient characteristics such as age, sex, disease duration, disease activity, Health Assessment Questionnaire score, erythrocyte sedimentation rate, C-reactive protein, and serological status regarding rheumatoid factor (RF) and anti-cyclic citrullinated protein antibodies (ACPA). Clinical responses were analyzed 6 months after RTX initiation using the European Alliance of Associations for Rheumatology criteria. Potential predictors associated with positive RTX response at 6 months were identified using a multivariate ordinal logistic regression model.

Results: The analysis showed that persistently active RA disease, Disease Activity Score with 28-joint count (DAS28) values at the treatment onset and after 3 months, along with erythrocyte sedimentation rate at treatment initiation, were negatively correlated with the response to RTX therapy (p < 0.05). All these correlations were statistically significant at the 99% confidence interval. The correlation and logistic regression analyses indicate that there are no significant association between RF and ACPA concerning therapy response, despite a higher number of RTX responders in the seropositive groups. Additionally, the study emphasizes the prognostic significance of the DAS28 value at treatment initiation in predicting therapy response at 6 months.

Conclusions: The optimal model for predicting RTX response at 6 months involves the interaction of all clinical factors examined in this study, as revealed by the analysis of multiple variables.