Antifungal prophylaxis in adult lung transplant recipients: Uncertainty despite 30 years of experience. A systematic review of the literature and network meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Invasive fungal infections (IFI), particularly invasive aspergillosis (IA), cause significant morbidity and mortality in lung transplant (LTx) recipients. The optimum strategy and antifungal agents for prevention are unclear. METHODS: We performed a comprehensive literature search, systematic review, and network meta-analysis using a frequentist framework to compare the efficacy of various antifungal drugs on the incidence of IA/IFI in the setting of universal prophylaxis or no prophylaxis following lung transplantation. RESULTS: We included 13 eligible studies comprising of 1515 LTx recipients and 12 different prophylaxis strategies/antifungal combinations. The greatest number of direct comparisons were between the inhaled amphotericin formulations. The top three ranked treatments were inhaled liposomal amphotericin B (L-AmB), inhaled amphotericin deoxycholate (AmBd), and itraconazole plus inhaled amphotericin B (AmB). Among the azoles, isavuconazole ranked highest. The certainty of the evidence, assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework, was very low. CONCLUSION: Although universal antifungal prophylaxis post lung transplantation is commonly used, robust data from randomized controlled trials (RCTs) to inform the choice of antifungal agent and prophylaxis strategy are lacking. This exploratory network meta-analysis provides insight into the probable relative effectiveness of various antifungal agents in preventing IA, and this analysis should serve as a guide when selecting antifungals to be assessed in a RCT.
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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 | 0.084 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".