Comparative Effectiveness of Abatacept Versus Tumor Necrosis Factor Inhibitors in Patients with Rheumatoid Arthritis Who Are Anti-CCP Positive in the United States Corrona Registry
Bibliographic record
Abstract
INTRODUCTION: Anti-citrullinated protein antibodies (ACPAs) are highly specific serological biomarkers that are indicative of a poor prognosis in patients with rheumatoid arthritis (RA). The effectiveness of biologic disease-modifying antirheumatic drugs (bDMARDs) with different mechanisms of action may vary, based on patients' serostatus. The aim of this study is to compare the effectiveness of abatacept versus tumor necrosis factor inhibitors (TNFis) in patients with RA who were anti-cyclic citrullinated peptide antibody positive (anti-CCP+). METHODS: Abatacept or TNFi initiators with anti-CCP+ status (≥ 20 U/ml) at or prior to treatment initiation were identified from a large observational US cohort (1 December 2005-31 August 2016). Using propensity score matching (1:1), stratified by prior TNFi use (0, 1 and ≥ 2), effectiveness at 6 months after initiation was evaluated. Primary outcome was mean change in Clinical Disease Activity Index (CDAI) score. Secondary outcomes included achievement of remission (CDAI ≤ 2.8), low disease activity/remission (CDAI ≤ 10), modified American College of Rheumatology 20/50/70 responses and mean change in modified Health Assessment Questionnaire score. RESULTS: After propensity score matching, the baseline characteristics between 330 pairs of abatacept and TNFi initiators (biologic naïve, n = 97; TNFi experienced, n = 233) were well balanced with absolute value standardized differences of ≤ 0.1. Both overall, and in the biologic-naïve cohort, there were no significant differences in mean change in CDAI score at 6 months. However, in the TNFi-experienced cohort, there was a significantly greater improvement in CDAI score at 6 months with abatacept versus TNFi initiators (p = 0.033). Secondary outcomes showed similar trends. CONCLUSIONS: Improvements in clinical disease activity were seen in anti-CCP+ abatacept and TNFi initiators. TNFi-experienced anti-CCP+ patients with RA had more improvement in disease activity with abatacept versus TNFis, whereas outcomes were similar between treatments in the overall population and in biologic-naïve patients. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01625650. FUNDING: This study is sponsored by Corrona, LLC and funded by Bristol-Myers Squibb. Bristol-Myers Squibb funded the publication of this manuscript.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".