Safety of Crossing Donor-specific Antibodies in Lung Transplantation
Bibliographic record
Abstract
BACKGROUND: Donor-specific antibodies (DSAs) have been associated with antibody-mediated rejection, chronic lung allograft dysfunction (CLAD), and increased mortality in lung transplant recipients. Our center performs transplants in the presence of DSA, and we sought to evaluate the safety of this practice with respect to graft loss, CLAD onset, and primary graft dysfunction (PGD). METHODS: We reviewed recipients transplanted from 2010 to 2017, classifying them as DSA positive (DSA + ) or negative. We used Kaplan-Meier estimation to test the association between DSA status and time to death or retransplant and time to CLAD onset. We further tested associations with severe PGD and rejection in the first year using logistic regression and Fisher exact testing. RESULTS: Three hundred thirteen patients met inclusion criteria, 30 (10%) of whom were DSA + . DSA + patients were more likely to be female, bridged to transplant, and receive induction therapy. There was no association between DSA status and time to death or retransplant (log rank P = 0.581) nor death-censored time to CLAD onset (log rank P = 0.278), but DSA + patients were at increased risk of severe PGD (odds ratio 2.88; 95% confidence interval, 1.10-7.29; P = 0.031) and more frequent antibody-mediated rejection in the first posttransplant year. CONCLUSIONS: Crossing DSA at time of lung transplant was not associated with an increased risk of death or CLAD in our cohort, but patients developed severe PGD and antibody-mediated rejection more frequently. However, these risks are likely manageable when balanced against the benefits of expanded access for sensitized candidates.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".