Next-Level Approach to Antibody-Mediated Rejection and T Cell-Mediated Rejection Diagnosis in Kidney Transplantation: Dynamic Duo of Urine CXCL10 and Donor-Derived Cell-Free DNA
Bibliographic record
Abstract
Background: Biopsies are required to diagnose rejection but they are invasive and difficult to use for monitoring. While there is evidence that donor derived cell free DNA (dd-cfDNA) performs well as a biomarker of antibody-mediated rejection (AMR), its ability to identify T cell mediated rejection (TCMR) remains unclear. In contrast, urine CXCL10 is a well-characterised biomarker of tubulitis. Due to these complementary properties, we hypothesized that use of these 2 biomarkers together would improve the diagnosis of rejection phenotypes marked predominantly by tubulitis, particularly TCMR. Methods: A single center exploratory study was conducted whereby 126 transplant biopsies (with paired plasma and urine) were selected. Banff criteria were followed to generate the following diagnostic categories: AMR (n=20), low and high grade TCMR (n=17) and normal histology (n=43). Urine CXCL10 was measured using the Meso Scale V-Plex assay. Cell free DNA was extracted from EDTA plasma samples and percent donor derived cell free DNA calculated using the CareDx AlloSeq cfDNA kit. Results: The AUC for AMR (versus normal) was 0.952 (0.893-1000) using dd-cfDNA. In contrast, the AUC for AMR was 0.595 (0.469-0.722) using urine CXCL10 and increased to 0.969 (0.923-1.000) when dd-cfDNA was added (p=1.71X10-8). For high grade TCMR, AUC using dd-cfDNA was 0.762 (0.562-0.963). AUC using urine CXCL10 was 0.681 (0.474-0.888) and increased to 0.792 (0.585-1.000) when dd-cfDNA was added (p=0.16). For low grade TCMR, AUC was 0.577 (0.442-0.711) using dd-cfDNA. AUC was 0.595 (0.424-0.767) using urine CXCL10 and increased to 0.652 (0.473-0.832) (p=0.32) when dd-cfDNA was added. Conclusion: Urine CXCL10 is a weaker diagnostic biomarker of AMR compared to dd-cfDNA. In contrast, when evaluating TCMR, there was no clear advantage of one biomarker over the other, though their combination may improve diagnosis. These findings require prospective multi-center studies. Funding: Private Foundation Support
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".