427.4: Combining Donor Derived Cell-Free DNA Fraction and Quantity To Detect Kidney Transplant Rejection Using Molecular Diagnoses
Bibliographic record
Abstract
Background: Donor-derived cell-free DNA (dd-cfDNA) fraction and quantity have both been shown to be associated with allograft rejection. The present study examined the relative predictive power of each of these variables to the combination of the two and developed an algorithm incorporating both variables to detect active rejection in renal allograft biopsies. Methods: The first 426 sequential indication biopsy samples collected from the Trifecta study (ClinicalTrials.gov # NCT04239703) with microarray-derived gene expression and dd-cfDNA results were included. After exclusions to simulate intended clinical use, 367 samples were analyzed. Biopsies were assessed using the Molecular Microscope Diagnostic System (MMDxTM) and histology (Banff 2019). Logistic regression analysis examined whether combining dd-cfDNA fraction and quantity adds predictive value to either alone. The first 149 sequential samples were used to develop a two-threshold algorithm, and the next 218 to validate the algorithm. Results: In regression, the combination of dd-cfDNA fraction and quantity was found to be significantly more predictive than either variable alone (p-value 0.009 and <0.0001). In the test set, the AUC of the two-variable system was 0.88 and performance of the two-threshold algorithm showed sensitivity 83.1% and specificity 81.0% using molecular diagnoses, and sensitivity 73.5% and specificity 80.8% using histology diagnoses. Conclusions: This prospective, biopsy-matched, multi-site dd-cfDNA study in kidney transplant patients found that the combination of dd-cfDNA fraction and quantity was more powerful than either dd-cfDNA fraction or quantity alone, and validated a novel two-threshold algorithm incorporating both variables.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".