Incidence Rates of Interstitial Lung Disease Events in Tofacitinib-Treated Rheumatoid Arthritis Patients
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). Interstitial lung disease (ILD) is an extra-articular manifestation of RA. We investigated incidence rates of ILD in patients with RA, receiving tofacitinib 5 or 10 mg twice daily, and identified potential risk factors for ILD. METHODS: This post hoc analysis comprised a pooled analysis of patients receiving tofacitinib 5 or 10 mg twice daily or placebo from 2 phase (P)1, 10 P2, 6 P3, 1 P3b/4, and 2 long-term extension studies. Interstitial lung disease events were adjudicated as "probable" (supportive clinical evidence) or "possible" (no supportive clinical evidence) compatible adverse events. Incidence rates (patients with events per 100 patient-years) were calculated for ILD events. RESULTS: Of 7061 patients (patient-years of exposure = 23,393.7), 42 (0.6%) had an ILD event; median time to ILD event was 1144 days. Incidence rates for ILD with both tofacitinib doses were 0.18 per 100 patient-years. Incidence rates generally remained stable over time. There were 17 of 42 serious adverse events (40.5%) of ILD; for all ILD events (serious and nonserious), 35 of 42 events (83.3%) were mild to moderate in severity. A multivariable Cox regression analysis identified age 65 years or older (hazard ratio 2.43 [95% confidence interval, 1.13-5.21]), current smokers (2.89 [1.33-6.26]), and Disease Activity Score in 28 joints-erythrocyte sedimentation rate score (1.30 [1.04-1.61]) as significant risk factors for ILD events. CONCLUSIONS: Across P1/2/3/4/long-term extension studies, incidence rates for ILD events were 0.18 following tofacitinib treatment, and ILD events were associated with known risk factors for ILD in RA.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".