Cost-effective Tapering Algorithm in Patients with Rheumatoid Arthritis: Combination of Multibiomarker Disease Activity Score and Autoantibody Status
Bibliographic record
Abstract
OBJECTIVE: To analyze the effect of a risk-stratified disease-modifying antirheumatic drug (DMARD)-tapering algorithm based on multibiomarker disease activity (MBDA) score and anticitrullinated protein antibodies (ACPA) on direct treatment costs for patients with rheumatoid arthritis (RA) in sustained remission. METHODS: The study was a posthoc retrospective analysis of direct treatment costs for 146 patients with RA in sustained remission tapering and stopping DMARD treatment, in the prospective randomized RETRO study. MBDA scores and ACPA status were determined in baseline samples of patients continuing DMARD (arm 1), tapering their dose by 50% (arm 2), or stopping after tapering (arm 3). Patients were followed over 1 year, and direct treatment costs were evaluated every 3 months. MBDA and ACPA status were used as predictors creating a risk-stratified tapering algorithm based on relapse rates. RESULTS: RA patients with a low MBDA score (< 30 units) and negative ACPA showed the lowest relapse risk (19%), while double-positive patients showed high relapse risk (61%). In ACPA-negative and MBDA-negative (< 30 units), and ACPA or MBDA single-positive (> 30 units) groups, DMARD tapering appears feasible. Considering only patients without flare, direct costs for synthetic and biologic DMARD in the ACPA/MBDA-negative and single positive groups (n = 41) would have been €372,245.16 for full-dose treatment over 1 year. Tapering and stopping DMARD in this low-risk relapse group allowed a reduction of €219,712.03 of DMARD costs. Average reduction of DMARD costs per patient was €5358.83. CONCLUSION: Combining MBDA score and ACPA status at baseline may allow risk stratification for successful DMARD tapering and cost-effective use of biologic DMARD in patients in deep remission as defined by the 28-joint count Disease Activity Score using erythrocyte sedimentation rate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".