Treatment of idiopathic pulmonary fibrosis: a network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease associated with high morbidity and mortality. Effective treatments for IPF are limited. Several recent studies have investigated novel therapeutic agents for IPF, but very few have addressed their comparative benefits and harms. METHODS: We performed a Bayesian network meta-analysis (NMA) to assess the effects of different treatments for IPF on mortality and serious adverse events (SAEs). We searched MEDLINE and EMBASE for randomized controlled trials (RCTs) up to August 2015. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach served to assess the certainty in the evidence of direct and indirect estimates. We calculated the surface under the cumulative ranking curve (SUCRA) for each treatment. We included parallel group RCTs, including factorial designs, but excluded quasi-randomized and cross-over trials. Studies were only included if they involved adult (≥18 years of age) patients with IPF as defined by the 2011 criteria and examined one of the 10 interventions of interest (ambrisentan, bosentan, imatinib, macitentan, N-acetylcysteine, nintedanib, pirfenidone, sildenafil, prednisone/azathioprine/N-acetylcysteine triple therapy, and vitamin K antagonist). RESULTS: A total of 19 RCTs (5,694 patients) comparing 10 different interventions with placebo and an average follow-up period of 1 year fulfilled the inclusion criteria. SUCRA analysis suggests nintedanib, pirfenidone, and sildenafil are the three treatments with the highest probability of reducing mortality in IPF. Indirect comparison showed no significant difference in mortality between pirfenidone and nintedanib (NMA OR, 1.05; 95% CrI, 0.45-2.78, moderate certainty of evidence), pirenidone and sildenafil (NMA OR, 2.26; 95% CrI, 0.44-13.17, low certainty of evidence), or nintedanib and sildenafil (NMA OR 2.40; 95% CrI, 0.47-14.66, low certainty of evidence). Sildenafil, pirfenidone, and nintedanib were ranked second, fourth, and sixth out of 10 for SAEs. CONCLUSION: In the absence of direct comparisons between treatment interventions, this NMA suggests that treatment with nintedanib, pirfenidone, and sildenafil extends survival in patients with IPF. The SAEs of these agents are similar to the other interventions and include mostly dermatologic and gastrointestinal manifestations. Head-to-head comparisons need to confirm these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".