Nintedanib in Rheumatoid Arthritis–Related Interstitial Lung Disease: Real-World Safety Profile and Risk of Side Effects and Discontinuation
Bibliographic record
Abstract
OBJECTIVE: Some concerns remain about the safety of nintedanib in patients with rheumatoid arthritis-related interstitial lung disease (RA-ILD), such as in the presence of comorbidities or in combination with biologic, targeted synthetic, and/or conventional synthetic disease-modifying antirheumatic drugs (DMARDs). In this multicenter study, we retrospectively evaluated the safety of nintedanib in a real-world population of patients with RA-ILD from the Italian Group for the Study of Early Arthritis (GISEA) registry and the possible role of comorbidities and DMARDs on drug safety and withdrawal. Our secondary aim was to investigate the causes of nintedanib discontinuation. METHODS: Sixty-five patients treated with nintedanib in accordance with the current therapeutic indications were enrolled in the study. Nintedanib was prescribed in combination with DMARDs and/or steroids in 62 patients (95.4%). RESULTS: The 12-month retention rate of nintedanib was 76.7% and the drug was effective in about 80% of patients with ≥ 6 months of follow-up. Adverse events (AEs) were recorded in 36 subjects (55.3%), and these were mainly gastroenteric. Thirty-one subjects required a reduction of the nintedanib dose; among them, a transient or permanent reduction of the daily dose of nintedanib allowed the continuation of the treatment in 22, whereas 15 (23.1%) withdrew from the drug. All reductions and discontinuations were owing to treatment-related AEs. Comorbidities were significantly associated with side effects in multivariate analysis, whereas AEs due to nintedanib were the main cause of discontinuation. CONCLUSION: Combination therapy with DMARDs did not reduce the safety and effectiveness of nintedanib, and AEs were the main cause of drug withdrawal or dose reduction, mainly owing to comorbidities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".