Cardiovascular (CV) Risk after Initiation of Abatacept versus TNF Inhibitors in Rheumatoid Arthritis Patients with and without Baseline CV Disease
Bibliographic record
Abstract
OBJECTIVE: To evaluate the cardiovascular safety of abatacept (ABA) versus tumor necrosis factor inhibitors (TNFi) in rheumatoid arthritis (RA) patients with and without underlying cardiovascular disease (CVD). METHODS: We identified RA patients with and without baseline CVD who initiated ABA or TNFi by using data from 2 large US insurance claims databases: Medicare (2008-2013) and Truven MarketScan (2006-2015). After stratifying by baseline CVD, ABA initiators were 1:1 propensity score (PS) matched to TNFi initiators to control for > 60 baseline covariates. Cox proportional hazards regression estimated the HR and 95% CI for a composite endpoint of CVD including myocardial infarction, stroke/transient ischemic stroke, or coronary revascularization in the PS-matched cohorts. HR from 2 databases were combined through an inverse variance-weighted fixed-effects model. RESULTS: We included 6102 PS-matched pairs of ABA and TNFi initiators from Medicare and 6934 pairs from MarketScan. Of these, 35.3% in Medicare and 14.0% in MarketScan had baseline CVD. HR (95% CI) for composite CVD in the overall ABA group versus TNFi was 0.67 (0.55-0.81) in Medicare and 1.08 (0.83-1.41) in MarketScan with the combined HR of 0.79 (0.67-0.92). Among patients with baseline CVD, the HR (95% CI) was 0.71 (0.55-0.92) in Medicare and 1.02 (0.68-1.51) in MarketScan, with the combined HR of 0.79 (0.64-0.98). CONCLUSION: In this large cohort of publicly or privately insured patients with RA in the United States, ABA was associated with a 20% reduced risk of CVD versus TNFi. While this observational study is subject to potential residual confounding, our results were consistent in patients with baseline CVD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".