Interferon Gamma ELISPOT Testing as a Risk-Stratifying Biomarker for Kidney Transplant Injury: Results From the CTOT-01 Multicenter Study
Bibliographic record
Abstract
Previous studies suggest that quantifying donor-reactive memory T cells prior to kidney transplantation by interferon gamma enzyme-linked immunosorbent spot assay (IFNγELISPOT) can assist in assessing risk of posttransplant allograft injury. Herein, we report an analysis of IFNγELISPOT results from the multicenter, Clinical Trials in Organ Transplantation-01 observational study of primary kidney transplant recipients treated with heterogeneous immunosuppression. Within the subset of 176 subjects with available IFNγELISPOT results, pretransplant IFNγELISPOT positivity surprisingly did not correlate with either the incidence of acute rejection (AR) or estimated glomerular filtration rate (eGFR) at 6- or 12-month. These unanticipated results prompted us to examine potential effect modifiers, including the use of T cell-depleting, rabbit anti-thymocyte globulin (ATG). Within the no-ATG subset, IFNγELISPOT(neg) subjects had higher 6- and 12-month eGFRs than IFNγELISPOT(pos) subjects, independent of biopsy-proven AR, peak PRA, human leukocyte antigen mismatches, African-American race, donor source, and recipient age or gender. In contrast, IFNγELISPOT status did not correlate with posttransplant eGFR in subjects given ATG. Our data confirm an association between pretransplant IFNγELISPOT positivity and lower posttransplant eGFR, but only in patients who do not receive ATG induction. Controlled studies are needed to test the hypothesis that ATG induction is preferentially beneficial to transplant candidates with high frequencies of donor-reactive memory T cells.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".