Risk of Lung Allograft Dysfunction Associated With Aspergillus Infection
Bibliographic record
Abstract
Background. We sought to determine whether invasive aspergillosis (IA) during the first year after lung transplantation increased the risk of chronic lung allograft dysfunction (CLAD). Methods. We retrospectively reviewed the records of 191 patients who underwent lung transplantation at our institution between January 2013 and December 2017. Screening for Aspergillus was with bronchial aspirates, bronchoalveolar lavage if indicated or during surveillance bronchoscopy, radiography, and computed tomography. We used Fine and Gray multivariable regression to identify potential risk factors for CLAD. Results. During the first posttransplant year, 72 patients had at least 1 deep-airway sample positive for Aspergillus; 63 were classified as having IA and were included in the study. Median number of endoscopies per patient during the first year was 9 (range, 1–44). Median time from transplantation to first Aspergillus-positive sample was 121 d. Bronchial aspirate samples and bronchoalveolar lavage fluid were positive in 71 and 44 patients, respectively. Aspergillus fumigatus (n = 36, 50%) predominated; bacterial samples were also positive in 22 (31%) patients. IA within 4 mo after transplantation was independently associated with CLAD development (subdistribution hazard ratio, 3.75; 95% confidence interval [CI], 1.61-8.73; P < 0.01) by regression analysis. Survival at 3 and 5 y conditional on 1-y CLAD-free survival was 37% (95% CI, 24%-58%), and 24% (95% CI, 11%-52%) in the IA <4 mo group compared to 65% (95% CI, 57%-73%) and 54% (95% CI, 43%-66%) in the non-IA group and to 69% (95% CI, 58%-83%) and 54% (95% CI, 35%-82%) in the IA ≥4 mo group, respectively (P < 0.01, logrank test). Conclusions. Our evaluation of de novo IA showed that this infection was most strongly associated with CLAD when found within 4 mo after transplantation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".