Risk Factors for Acute Rejection in the First Year after Lung Transplant. A Multicenter Study
Bibliographic record
Abstract
Abstract Rationale Acute rejection, manifesting as lymphocytic inflammation in a perivascular (acute perivascular rejection [AR]) or peribronchiolar (lymphocytic bronchiolitis [LB]) distribution, is common in lung transplant recipients and increases the risk for chronic graft dysfunction. Objectives To evaluate clinical factors associated with biopsy-proven acute rejection during the first post-transplant year in a present-day, five-center lung transplant cohort. Methods We analyzed prospective diagnoses of AR and LB from over 2,000 lung biopsies in 400 newly transplanted adult lung recipients. Because LB without simultaneous AR was rare, our analyses focused on risk factors for AR. Multivariable Cox proportional hazards models were used to assess donor and recipient factors associated with the time to the first AR occurrence. Measurements and Main Results During the first post-transplant year, 53.3% of patients experienced at least one AR episode. Multivariable proportional hazards analyses accounting for enrolling center effects identified four or more HLA mismatches (hazard ratio [HR], 2.06; P ≤ 0.01) as associated with increased AR hazards, whereas bilateral transplantation (HR, 0.57; P ≤ 0.01) was associated with protection from AR. In addition, Wilcoxon rank-sum analyses demonstrated bilateral (vs. single) lung recipients, and those with fewer than four (vs. more than four) HLA mismatches demonstrated reduced AR frequency and/or severity during the first post-transplant year. Conclusions We found a high incidence of AR in a contemporary multicenter lung transplant cohort undergoing consistent biopsy sampling. Although not previously recognized, the finding of reduced AR in bilateral lung recipients is intriguing, warranting replication and mechanistic exploration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".