Antibody-Mediated Microcirculation Injury Is the Major Cause of Late Kidney Transplant Failure: Response to Dr. Loupy et al.
Bibliographic record
Abstract
We thank the authors for their comments on our article (1Einecke G Sis B Reeve J et al.Antibody-mediated microcirculation injury is the major cause of late kidney transplant failure.Am J Transplant. 2009; 9: 2520-2531Abstract Full Text Full Text PDF PubMed Scopus (536) Google Scholar). Their recent study (2Loupy A Suberbielle-Boissel C Hill GS et al.Outcome of subclinical antibody-mediated rejection in kidney transplant recipients with preformed donor-specific antibodies.Am J Transplant. 2009; 9: 2561-2570Abstract Full Text Full Text PDF PubMed Scopus (262) Google Scholar) adds yet another piece of evidence that some C4d-negative kidneys share features of antibody-mediated injury with biopsies that fulfill the criteria for C4d-positive antibody-mediated rejection (ABMR), suggesting that C4d positivity may be only the extreme of the ABMR spectrum. The combination of C4d-negative ABMR plus typical C4d positive ABMR accounts for the majority of kidney transplant losses after a biopsy for cause, offering improved prediction of graft outcomes and defining ABMR clinically even in C4d-negative biopsies in patients with circulating antibodies. The C4d-negative cases with anti-HLA and microcirculation changes have previously been identified by Banff as ‘suspicious for ABMR,’ and we now suggest that they be designated ABMR at future Banff meetings given the similarities in microcirculation lesions and prognosis. The presence and predictive value of microcirculation lesions in the absence of C4d in protocol biopsies in clinically stable patients suggests that these histologic features occur at an early stage in the progression of antibody-mediated graft injury. Predicting which renal transplants are at risk for antibody-mediated graft deterioration is an unmet need, and such patients may be the most important to detect, before potentially irreversible changes appear. As such, more sensitive diagnostic criteria for the detection of ABMR even in clinically stable patients are needed. However, let us not forget that changes and adjustments to histologic diagnostic criteria are only a tool to help with the key issues that remain to be solved: understanding the pathogenesis of antibody-mediated graft injury and effective treatment strategies. Many transplants with evidence of deterioration do not get biopsied because clinicians believe that biopsies offer too little clinical utility. The emergence of a new definition of ABMR and realization of its importance could potentially change clinical practice, with a more aggressive use of anti-HLA testing and biopsies, but the real impetus for change will come when effective strategies to prevent and treat late ABMR are identified, since this would be the best leverage for preventing failure of kidney transplants.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".