Poor Patient and Graft Outcome After Induction Treatment by Antithymocyte Globulin in Recipients of a Kidney Graft After Nonrenal Organ Transplantation
Bibliographic record
Abstract
BACKGROUND: End-stage renal failure occurs in a substantial number of patients having received a nonrenal transplantation (NRT), for whom a kidney transplantation is needed. The medical strategy regarding the use of immunosuppression (IS) for a kidney graft in patients after an NRT is not well established. The prekidney grafts long-term IS advocates for a mild induction, such as using anti-IL-2R antibodies, whereas addition of new incompatibilities and anti-HLA preimmunization may suggest using stronger IS such as induction by polyclonal antithymocyte globulins (ATG). METHODS: We performed Cox multivariate and propensity score analysis of our validated transplant database to study the impact of the type of induction therapy on kidney graft survival of recipients of a kidney graft after NRT. RESULTS: We report here that kidney transplantation after NRT treated with an ATG induction has a poorer outcome (kidney and recipient survival) than that with an anti-IL-2R induction. After accounting for potential baseline differences with a multivariate Cox model, or by adjusting on a propensity score, we found that despite patients having received ATG cumulate more risk factors, ATG appears independently involved. As animal-derived biotherapeutics induce antiglycan antibodies and particularly anti-N-glycolylneuraminic acid (Neu5Gc) IgGs which may activate endothelial cells in patients and grafts, we also investigated the magnitude and the nature of the anti-Neu5Gc elicited by the induction and showed that induction was associated with a shift in anti-Neu5Gc IgG repertoire. Possible reasons and mechanisms of a deleterious ATG usage in these patients are discussed. CONCLUSIONS: Our study suggests that ATG induction after a kidney transplantation in recipients already under maintenance IS for a NRT should be used cautiously.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".