Donor-derived cell-free DNA significantly improves rejection yield in kidney transplant biopsies
Bibliographic record
Abstract
Donor-derived cell-free DNA (dd-cfDNA) is a biomarker that enables the early detection of immune-mediated graft injury. This study evaluated the clinical utility of dd-cfDNA in predicting the presence of biopsy-proven rejection (BPAR). We analyzed 1070 biopsies from 1743 kidney transplant recipients enrolled in the prospective, multicenter Kidney Allograft Outcomes AlloSure Registry. Biopsies were grouped into surveillance or for-cause groups and stratified by dd-cfDNA status: elevated, nonelevated, or not tested. Rejection yield was significantly higher when dd-cfDNA was elevated: 39% vs 7% in the surveillance group and 47% vs 12% in the for-cause group (P < .0001). Biopsies with elevated dd-cfDNA and rejection diagnoses more frequently demonstrated antibody-mediated rejection and mixed rejection, whereas biopsies performed with nonelevated dd-cfDNA most often showed no rejection, borderline, or T cell-mediated rejection 1A. The area under the receiving operating characteristic curve for BPAR detection was 0.789. These findings demonstrate that dd-cfDNA levels can improve the pretest probability of BPAR in both surveillance and for-cause settings. Therefore, dd-cfDNA can optimize biopsy utilization by identifying kidney transplant patients who are most likely to have histologic rejection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".