Clinical and Molecular Data to Predict Flares in DMARD optimization in Rheumatoid Arthritis: A Randomised, Controlled, Open-label, Non-inferiority Trial

Objectives

Optimization of biological DMARDs (bDMARDs) in patients with rheumatoid arthritis (RA) may be feasible in those who have maintained remission for at least six months. However, this approach carries the risk of disease flare, underscoring the need for reliable predictors to guide clinical decisions.

Methods

The OPTIBIO trial (EudraCT 2012-004482-40) was a phase IV, randomized, open-label, non-inferiority study conducted in five hospitals in Spain. RA patients in sustained remission on stable bDMARD therapy were randomized 1:1 to standard care or dose reduction. The primary outcome was to compare to proportion of joint flare at 12 months by a non-inferiority analysis analyzed by the intention-to-treat principle and identify predictors for flare and sustained remission.

Results

195 patients were randomized: 99 to the control group and 96 to the optimization group. Thirty-nine flares occurred (optimization: 22.7%, control: 17.2%), with a risk difference of -5.5% (95% CI: -16.8% to 5.7%; P = 0.33). Two predictive models were developed: one for flares (AUC: 0.84) including 3v-DAS28-CRP, VAS pain, erosions, systolic blood pressure, and hemoglobin, and another for sustained remission (AUC: 0.77) including 3v-DAS28-CRP, age, and rheumatoid factor. Adding molecular biomarkers improved AUCs to 0.91 and 0.88, respectively. No significant differences in adverse events were observed.

Conclusion

bDMARD dose optimization was non-inferior to standard therapy on the flare rate but demonstrated similar safety. Predictive models for remission and flares were developed, which may help select patients to ensure safe implementation of this strategy, highlighting the need for personalized treatment.

Key messages

• bDMARD dose optimization was non-inferior to standard therapy on the flare

• Clinical models classified patients in terms of disease severity

• Incorporation of molecular markers to clinical models improved their prediction power