Deceased donor kidney transplantation across donor-specific antibody barriers: predictors of antibody-mediated rejection
Bibliographic record
Abstract
BACKGROUND: Apheresis-based desensitization allows for successful transplantation across major immunological barriers. For donor-specific antibody (DSA)- and/or crossmatch-positive transplantation, however, it has been shown that even intense immunomodulation may not completely prevent antibody-mediated rejection (ABMR). METHODS: In this study, we evaluated transplant outcomes in 101 DSA+ deceased donor kidney transplant recipients (transplantation between 2009 and 2013; median follow-up: 24 months) who were subjected to immunoadsorption (IA)-based desensitization. Treatment included a single pre-transplant IA session, followed by anti-lymphocyte antibody and serial post-transplant IA. In 27 cases, a positive complement-dependent cytotoxicity crossmatch (CDCXM) was rendered negative immediately before transplantation. Seventy-four of the DSA+ recipients had a negative CDCXM already before IA. RESULTS: Three-year death-censored graft survival in DSA+ patients was significantly worse than in 513 DSA- recipients transplanted during the same period (79 versus 88%, P = 0.008). Thirty-three DSA+ recipients (33%) had ABMR. While a positive baseline CDCXM showed only a trend towards higher ABMR rates (41 versus 30% in CDCXM- recipients, P = 0.2), DSA mean fluorescence intensity (MFI) in single bead assays significantly associated with rejection, showing 20 versus 71% ABMR rates at <5000 versus >15 000 peak DSA MFI. The predictive value of MFI was moderate, with the highest accuracy at a median of 13 300 MFI (after cross-validation: 0.72). Other baseline variables, including CDC assay results, human leukocyte antigen mismatch, prior transplantation or type of induction treatment, did not add independent predictive information. CONCLUSIONS: IA-based desensitization failed to prevent ABMR in a considerable number of DSA+ recipients. Assessing DSA MFI may help stratify risk of rejection, supporting its use as a guide to organ allocation and individualized treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".