Nintedanib in Patients With Autoimmune Disease–Related Progressive Fibrosing Interstitial Lung Diseases: Subgroup Analysis of the <scp>INBUILD</scp> Trial
Bibliographic record
Abstract
OBJECTIVE: To analyze the efficacy and safety of nintedanib in patients with fibrosing autoimmune disease-related interstitial lung diseases (ILDs) with a progressive phenotype. METHODS: The INBUILD trial enrolled patients with a fibrosing ILD other than idiopathic pulmonary fibrosis, with diffuse fibrosing lung disease of >10% extent on high-resolution computed tomography, forced vital capacity percent predicted (FVC%) ≥45%, and diffusing capacity of the lungs for carbon monoxide percent predicted ≥30% to <80%. Patients fulfilled protocol-defined criteria for progression of ILD within the 24 months before screening, despite management deemed appropriate in clinical practice. Subjects were randomized to receive nintedanib or placebo. We assessed the rate of decline in FVC (ml/year) and adverse events (AEs) over 52 weeks in the subgroup with autoimmune disease-related ILDs. RESULTS: Among 170 patients with autoimmune disease-related ILDs, the rate of decline in FVC over 52 weeks was -75.9 ml/year with nintedanib versus -178.6 ml/year with placebo (difference 102.7 ml/year [95% confidence interval 23.2, 182.2]; nominal P = 0.012). No heterogeneity was detected in the effect of nintedanib versus placebo across subgroups based on ILD diagnosis (P = 0.91). The most frequent AE was diarrhea, reported in 63.4% and 27.3% of subjects in the nintedanib and placebo groups, respectively. AEs led to permanent discontinuation of trial drug in 17.1% and 10.2% of subjects in the nintedanib and placebo groups, respectively. CONCLUSION: In the INBUILD trial, nintedanib slowed the rate of decline in FVC in patients with progressive fibrosing autoimmune disease-related ILDs, with AEs that were manageable for most patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".