Pretransplant Calculated Panel Reactive Antibody in the Absence of Donor-Specific Antibody and Kidney Allograft Survival
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Panel reactive antibody informs the likelihood of finding an HLA-compatible donor for transplant candidates, but has historically been associated with acute rejection and allograft survival because testing methods could not exclude the presence of concomitant donor-specific antibodies. Despite new methods to exclude donor-specific antibodies, panel reactive antibody continues to be used to determine the choice of induction and maintenance immunosuppression. The study objective was to determine the clinical relevance of panel reactive antibody in the absence of donor-specific antibodies. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Retrospective observational study of kidney allograft survival among 4058 zero HLA-A-, B-, DR-, and DQB1-mismatched transplant recipients without antibodies to donor kidney antigens encoded by these HLA gene loci. RESULTS: Among 4058 first and repeat transplant recipients, patients with calculated panel reactive antibody (cPRA) 1%-97% were not at higher risk of transplant failure, compared with patients with cPRA of 0% (death censored graft loss: hazard ratio, 1.07; 95% confidence interval, 0.82 to 1.41). Patients with cPRA ≥98% had a higher risk of graft loss from any cause including death (hazard ratio, 1.39; 95% confidence interval, 1.08 to 1.79) and death censored allograft failure (hazard ratio, 1.78; 95% confidence interval, 1.27 to 2.49). In stratified analyses, the higher risk of graft loss among patients with cPRA ≥98% was only observed among repeat, but not first, transplant recipients. In subgroup analysis, there was no association between cPRA and graft loss among living related transplant recipients. CONCLUSIONS: Calculated panel reactive antibody is poorly associated with post-transplant immune reactivity to the allograft in the absence of donor-specific antibody. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_01_25_CJN13640820_final.mp3.
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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) | 0.000 | 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) | 0.001 | 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".