Longterm, Real-world Safety of Adalimumab in Rheumatoid Arthritis: Analysis of a Prospective US-based Registry
Bibliographic record
Abstract
OBJECTIVE: To assess longterm safety in a US cohort of patients with rheumatoid arthritis (RA) treated with adalimumab (ADA) in real-world clinical care settings. METHODS: This observational study analyzed the longterm incidence of safety outcomes among patients with RA initiating ADA, using data from the Corrona RA registry. Patients were adults (≥ 18 yrs) who initiated ADA treatment between January 2008 and June 2017, and who had at least 1 followup visit. RESULTS: In total, 2798 ADA initiators were available for analysis, with a mean age of 54.5 years, 77% female, and mean disease duration of 8.3 years. Nearly half (48%) were biologic-naive, and 9% were using prednisone ≥ 10 mg at ADA initiation. The incidence rates per 100 person-years for serious infections, congestive heart failure requiring hospitalization, malignancy (excluding nonmelanoma skin cancer), and all-cause mortality were 1.86, 0.15, 0.64, and 0.33, respectively. The incidence of serious infections was higher in the first year of therapy (3.44, 95% CI 2.45-4.84) than in subsequent years, while other measured adverse effects did not vary substantially by duration of exposure. The median time to ADA discontinuation was 11 months, while the median time to first serious infection among those experiencing a serious infection event was 12 months. CONCLUSION: Analysis of longterm data from this prospective real-world registry demonstrated a safety profile consistent with previous studies in patients with RA. This analysis did not identify any new safety signals associated with ADA treatment and provides guidance for physicians prescribing ADA for extended periods.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".