Survival in Sensitized Lung Transplant Recipients With Perioperative Desensitization
Bibliographic record
Abstract
Donor-specific HLA antibodies (DSA) have an adverse effect on short-term and long-term lung transplant outcomes. We implemented a perioperative strategy to treat DSA-positive recipients, leading to equivalent rejection and graft survival outcomes. Pretransplant DSA were identified to HLA-A, B, C, DR and DQ antigens. DSA-positive patients were transplanted if panel reactive antibody (PRA) ≥30% or medically urgent and desensitized with perioperative plasma exchange, intravenous immune globulin, antithymocyte globulin (ATG), and mycophenolic acid (MPA). PRA-positive/DSA-negative recipients received MPA. Unsensitized patients received routine cyclosporine, azathioprine and prednisone without ATG. From 2008-2011, 340 lung-only first transplants were performed: 53 DSA-positive, 93 PRA-positive/DSA-negative and 194 unsensitized. Thirty-day survival was 96 %/99%/96% in the three groups, respectively. One-year graft survival was 89%/88%/86% (p = 0.47). DSA-positive and PRA-positive/DSA-negative patients were less likely to experience any ≥ grade 2 acute rejection (9% and 9% vs. 18% unsensitized p = 0.04). Maximum predicted forced expiratory volume (1 s) (81%/74%/76%, p = NS) and predicted forced vital capacity (81%/77%/78%, respectively, p = NS) were equivalent between groups. With the application of this perioperative treatment protocol, lung transplantation can be safely performed in DSA/PRA-positive patients, with similar outcomes to unsensitized recipients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".