Molecular Microscope Strategy to Improve Risk Stratification in Early Antibody-Mediated Kidney Allograft Rejection
Bibliographic record
Abstract
Antibody-mediated rejection (ABMR) is the leading cause of kidney allograft loss. We investigated whether the addition of gene expression measurements to conventional methods could serve as a molecular microscope to identify kidneys with ABMR that are at high risk for failure. We studied 939 consecutive kidney recipients at Necker Hospital (2004-2010; principal cohort) and 321 kidney recipients at Saint Louis Hospital (2006-2010; validation cohort) and assessed patients with ABMR in the first 1 year post-transplant. In addition to conventional features, we assessed microarray-based gene expression in transplant biopsy specimens using relevant molecular measurements: the ABMR Molecular Score and endothelial donor-specific antibody-selective transcript set. The main outcomes were kidney transplant loss and progression to chronic transplant injury. We identified 74 patients with ABMR in the principal cohort and 54 patients with ABMR in the validation cohort. Conventional features independently associated with failure were donor age and humoral histologic score (g+ptc+v+cg+C4d). Adjusting for conventional features, ABMR Molecular Score (hazard ratio [HR], 2.22; 95% confidence interval [95% CI], 1.37 to 3.58; P=0.001) and endothelial donor-specific antibody-selective transcripts (HR, 3.02; 95% CI, 1.00 to 9.16; P<0.05) independently associated with an increased risk of graft loss. The results were replicated in the independent validation group. Adding a gene expression assessment to a traditional risk model improved the stratification of patients at risk for graft failure (continuous net reclassification improvement, 1.01; 95% CI, 0.57 to 1.46; P<0.001; integrated discrimination improvement, 0.16; P<0.001). Compared with conventional assessment, the addition of gene expression measurement in kidney transplants with ABMR improves stratification of patients at high risk for graft loss.
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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) | 0.000 | 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) | 0.000 | 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".