Rheumatoid Arthritis (RA)
Conditions
Keywords
Rheumatoid arthritis, Treatment Optimization, TNF Inhibitors (TNFi), Biologic Therapy, Personalized Medicine, Precision Medicine
Brief summary
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.
Detailed description
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.
Interventions
DEVICEPredictive Model-Guided Decision Strategy
Treatment optimization decisions are guided by the OPTIBIO predictive model, which integrates clinical variables with biomarker data derived from peripheral blood, including protein expression and genetic information. The model provides an individualized estimation of the risk of disease flare associated with treatment reduction and generates a recommendation on whether to maintain or taper biological therapy.
Sponsors
Francisco J. Blanco
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE (Subject, Investigator)
Masking description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of patients maintaining sustained remission. | Up to 12 months | Proportion of patients who remain in sustained remission throughout the entire follow-up period, defined as DAS28-CRP \< 2.6 based on tender joint count (28 joints), swollen joint count (28 joints), C-reactive protein levels, and patient global assessment |
| Incidence of adverse events | Baseline to 70 days after last dose | Incidence and characteristics of adverse events, including serious infections requiring systemic antibiotics or hospitalization, serious treatment-related adverse events, and specific adverse reactions (e.g., infusion or injection reactions), including severity. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of patients achieving sustained acceptable therapeutic target | Up to 12 months | Proportion of patients maintaining an acceptable therapeutic target throughout follow-up, defined as low disease activity (DAS28-CRP \< 3.2) and absence of persistent inflammation in major joints (shoulders, elbows, hips, and knees). |
| Proportion of patients experiencing disease flare (0-6 months). | Up to 6 months | Proportion of patients experiencing disease flare during the first 6 months of follow-up, defined as DAS28-CRP \> 2.6. |
| Proportion of patients experiencing disease flare (6-12 months) | 6 to 12 months | Proportion of patients experiencing disease flare between months 6 and 12 of follow-up, defined as DAS28-CRP \> 2.6. |
| Number of disease flares (0-6 months) | Up to 6 months | Total number of disease flares occurring during the first 6 months of follow-up. |
| Number of disease flares (6-12 months) | 6 to 12 months | Total number of disease flares occurring between months 6 and 12 of follow-up. |
| Proportion of patients achieving acceptable therapeutic target at final visit | At 12 months | Proportion of patients meeting the acceptable therapeutic target definition at the last study visit. |
| Proportion of patients in remission at final visit | At 12 months | Proportion of patients in remission at the last study visit, defined as DAS28-CRP \< 2.6. |
| Time to disease flare | Up to 12 months | Time from baseline to the first occurrence of disease flare, defined as DAS28-CRP \> 2.6. |
| Change in clinical disease activity parameters (joint count) | Baseline to 12 months | Changes over time in clinical parameters: tender joint count (28 joints). |
| Change in clinical disease activity parameters: patient and physician global assessment | Baseline to 12 months | Changes over time in clinical parameters: patient and physician global assessment (0-100 mm scale). |
| Change in clinical disease activity parameters: CRP | Baseline to 12 months | Changes over time in clinical parameters: acute phase reactants, CRP (mg/L) |
| Change in health-related quality of life (EQ-5D-5L) | Baseline to 12 months | Change in health-related quality of life measured using the EuroQol 5-Dimension 5-Level (EQ-5D-5L) questionnaire. The score ranges from 0 to 1, where 1 represents perfect health |
| Change in functional disability (HAQ) | Baseline to 12 months | Change in functional disability assessed using the Health Assessment Questionnaire (HAQ). The HAQ score can range from 0 (no disability) to 3 (maximum disability). |
| Direct and indirect healthcare costs and cost-effectiveness | Up to 12 months | Assessment of direct and indirect healthcare costs and incremental cost-effectiveness and cost-utility ratios. |
| Use of concomitant medication | Up to 12 months | Use of analgesics, non-steroidal anti-inflammatory drugs, and corticosteroids, including route of administration. |
| Radiographic structural damage progression | Baseline and 12 months | Progression of structural damage assessed by blinded independent evaluators using the modified Sharp/van der Heijde scoring method on radiographs of hands, wrists, and feet. |
| Change in clinical disease activity parameters (ESR) | Baseline to 12 months | Changes over time in clinical parameters: acute phase reactants, ESR (mm/h). |
Countries
Spain
Contacts
CONTACTFrancisco J. Blanco, MD, PhD
PRINCIPAL_INVESTIGATORFrancisco J. Blanco, MD, PhD
Complejo Hospitalario Universitario de A Coruña
Outcome results
None listed