Incidence and Risk Factors for Serious Infection in Patients with Rheumatoid Arthritis Treated with Tumor Necrosis Factor Inhibitors: A Report from the Registry of Japanese Rheumatoid Arthritis Patients for Longterm Safety
Bibliographic record
Abstract
OBJECTIVE: To compare tumor necrosis factor-α (TNF-α) inhibitors to nonbiological disease-modifying antirheumatic drugs (DMARD) for the risk of serious infection in Japanese patients with rheumatoid arthritis (RA). METHODS: Serious infections occurring within the first year of the observation period were examined using the records for patients recruited to the Registry of Japanese Rheumatoid Arthritis Patients for Longterm Safety (REAL), a hospital-based prospective cohort of patients with RA. The analysis included 1144 patients, 646 of whom were treated with either infliximab or etanercept [exposed group: 592.4 patient-years (PY)]. The remaining 498 patients received nonbiological DMARD with no biologics (unexposed group: 454.7 PY). RESULTS: In the unexposed group, the incidence rate for all serious adverse events (SAE) was 9.02/100 PY and for serious infections, 2.64/100 PY. In the exposed group, SAE occurred in 16.04/100 PY and serious infections in 6.42/100 PY. The crude incidence rate ratio comparing serious infections in the exposed group with the unexposed group was 2.43 (95% CI 1.27-4.65), a significant increase. A multivariate analysis revealed that the use of TNF inhibitors is a significant independent risk factor for serious infection (relative risk 2.37, 95% CI 1.11-5.05, p = 0.026). CONCLUSION: Our study has provided the first epidemiological data on Japanese patients with RA for the safety of TNF inhibitors compared to nonbiological DMARD for up to 1 year of treatment. Anti-TNF therapy was associated with a significantly increased risk for serious infections, compared to treatment with nonbiological DMARD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".