This study aims to evaluate a new tool designed to help doctors decide whether it is safe to reduce medication in patients with rheumatoid arthritis (RA) who are in remission.

Rheumatoid arthritis is a chronic inflammatory disease that affects the joints, causing pain, stiffness, and reduced mobility. Many patients receive long-term treatment with biological drugs to control the disease. When the disease is well controlled (remission), doctors may gradually reduce the medication dose. However, deciding when and in whom to reduce treatment is currently based on experience and trial-and-error.

The study evaluates a predictive tool (called OPTIBIO) that uses information from blood samples, genetic data, and clinical characteristics to estimate the risk that the disease will flare up if treatment is reduced.

Participants in the study will be randomly assigned to one of two groups:

• In one group, the decision to reduce medication will be made by their usual doctor.
• In the other group, the decision will be guided by the predictive tool.

The study lasts 12 months and includes several hospital visits. During these visits, participants will:

• Answer questionnaires about their health and quality of life
• Have physical examinations
• Provide blood samples for routine tests and additional research purposes
• Possibly undergo joint ultrasound (if they consent) Some additional blood samples may be stored in authorized biobanks for future research related to rheumatoid arthritis, but only if participants explicitly agree. These samples will be coded to protect personal identity and will only be used in ethically approved research projects.

Participation in the study is entirely voluntary. Participants can choose which procedures they agree to and may withdraw at any time without affecting their medical care.

The study may not provide direct benefit to participants, but it could help improve future treatment decisions and the overall management of rheumatoid arthritis.

Background Rheumatoid arthritis (RA) is a chronic, immune-mediated inflammatory disease characterized by persistent synovitis, progressive joint damage, and reduced quality of life. The introduction of biological therapies, particularly tumor necrosis factor inhibitors (TNFi), has substantially improved disease outcomes, allowing many patients to achieve sustained remission.

In patients who reach remission, clinical guidelines recommend considering treatment optimization strategies, including dose tapering or discontinuation. However, in routine clinical practice, such decisions remain largely empirical and are primarily based on physician judgment. This approach introduces clinical uncertainty, as treatment reduction may lead to disease reactivation in a subset of patients, while continued treatment may expose patients to unnecessary risks and increase healthcare costs.

Rationale There is a clear unmet need for tools that support personalized treatment decisions in patients with RA in remission. A reliable method to predict the risk of disease flare could enable clinicians to better identify patients in whom treatment reduction can be safely implemented.

The OPTIBIO model has been developed as a predictive tool to address this need. It integrates clinical variables with biomarker data derived from peripheral blood, including protein expression, cellular components, and genetic information. By combining these data sources, the model aims to provide individualized risk predictions of disease reactivation following treatment optimization.

Study Purpose The purpose of this study is to evaluate the clinical utility of the OPTIBIO predictive model when incorporated into routine clinical decision-making, compared with standard practice.

The study assesses whether use of the model can support safer and more effective treatment optimization in patients with rheumatoid arthritis in remission receiving TNFi therapy.

Scientific and Clinical Contribution In addition to its clinical focus, the study includes the prospective collection of clinical data and biological samples to further investigate biomarkers associated with disease activity and relapse. These data will contribute to improving the predictive performance of the OPTIBIO model and to identifying novel molecular and cellular signatures associated with disease reactivation.

With participant consent, residual biological samples may be stored in authorized biobanks for future research. These samples may be used in ethically approved studies related to rheumatoid arthritis, contributing to a better understanding of disease mechanisms and to the development of new diagnostic and therapeutic approaches.

Health and Economic Relevance The study also addresses the broader impact of treatment optimization strategies on healthcare systems. By collecting data on healthcare resource utilization, it aims to explore the potential cost-effectiveness of incorporating predictive tools into routine care.

This is particularly relevant in chronic diseases such as RA, where long-term treatment costs and resource utilization are significant, and where more efficient, personalized treatment strategies could have substantial clinical and economic benefits.

Expected Impact This study is expected to generate evidence on the usefulness of a biomarker-based predictive approach to guide treatment decisions in rheumatoid arthritis. The implementation of such tools has the potential to improve patient outcomes, reduce the risk of disease flare, minimize unnecessary treatment exposure, and support more efficient use of healthcare resources.

Inclusion Criteria:

• Adults aged ≥18 years.
• Diagnosis of rheumatoid arthritis according to either the 1987 American College of Rheumatology (ACR) criteria or the 2010 ACR/EULAR classification criteria.
• Clinical remission for at least 6 months prior to the baseline visit, defined as DAS28-CRP < 2.6.
• Receiving biological anti-TNF therapy (infliximab, adalimumab, etanercept, golimumab, or certolizumab).
• Ability and willingness to provide written informed consent to participate in the study.

Exclusion Criteria:

• Patients in whom biological therapy was prescribed due to systemic manifestations of rheumatoid arthritis.
• Patients with rheumatoid arthritis and any known associated condition that may interfere with the assessment of study outcomes (e.g., fibromyalgia or concomitant chronic inflammatory diseases).
• Patients receiving chronic anti-TNF biological therapy who are already undergoing treatment tapering or are on reduced or extended dosing regimens prior to study inclusion.

Three levels of blinding are implemented:

• Participants and blinded consulting investigators are aware of whether treatment has been optimized, but are not informed about the decision-making process that led to treatment optimization.
• Blinded outcome assessors are not aware of either the treatment optimization status or the decision-making process.
• Non-blinded investigators have full knowledge of both treatment optimization and the decision-making process.