SaO014ALLOGRAFT GENE EXPRESSION ASSESSMENT PREDICTS GRAFT LOSS IN KIDNEY RECIPIENTS WITH CHRONIC ANTIBODY-MEDIATED REJECTION
Bibliographic record
Abstract
INTRODUCTION: Chronic antibody-mediated rejection (cAMR) is a major contributor to kidney allograft loss. However, prognostic heterogeneity has not been addressed. We investigated whether mechanistically-informed prediction of allograft loss could identify patients with different outcomes. METHODS: We prospectively enrolled all adult kidney recipients from two centers at the time of the first post-transplant allograft biopsy showing cAMR according to Banff criteria (2005-2016). All patients were assessed for cAMR phenotype based on histology, immunochemistry and gene expression using Nanostring technology on formalin-fixed paraffin-embedded tissue for 209 pathogenesis-selected genes previously identified for their relevance to transplant diagnosis. All patients underwent anti-HLA antibody, GFR and proteinuria evaluation at the time of cAMR diagnosis, and were followed up to September 2018. RESULTS: Among 151 patients meeting inclusion criteria, cAMR was diagnosed at a median time of 23 (IQR, 10-58) months after transplantation Cross-validated supervised principal component analysis identified two independent linear combinations of 20 genes associated with allograft loss, which were mainly related to B cell immunity and immunoglobulin-mediated immune response. We built a prognostic gene score based on the two components that exhibited good optimism-corrected calibration and greater time-dependent accuracy (5-year AUC of 0.80) than clinical and histological parameters for predicting allograft loss (5-year AUC for GFR, proteinuria and cg score: 0.67, 0.70 and 0.65, respectively). The prognostic gene score identified three groups of patients: low-score patients (n=22) with a 5-year allograft survival of 95%, intermediate-score patients (n=65) with a 5-year allograft survival of 64% and high-score patients (n=64) with a 5-year graft survival of 29% (p<0.001). In multivariable analysis, the prognostic gene score predicted graft loss independently of clinical, immunological and histological parameters (adjusted HR: 1.70, 95%CI: 1.22-2.36, p=0.002). CONCLUSIONS: By addressing prognostic heterogeneity in cAMR at the molecular level, we defined a pathogenesis-based prognostic score that outperformed clinical, immunological and histological parameters for predicting kidney allograft loss.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".