Impact of Belatacept Conversion on Renal Function, Histology, and Gene Expression in Kidney Transplant Patients With Chronic Active Antibody-mediated Rejection
Bibliographic record
Abstract
BACKGROUND: Here, we present our initial experience with a prospective protocol of belatacept conversion in patients with chronic active antibody-mediated rejection (caAMR) and a high degree of chronicity at the time of diagnosis. METHODS: We converted 19 patients (mean age, 45 ± 12 y) with biopsy-proven caAMR from tacrolimus to belatacept at a median of 44 months post-kidney transplant. RESULTS: At a median of 29 months (interquartile range, 16-46 mo) postconversion, death-censored graft and patient survivals were 89% and 95%, respectively. When compared to a 1:2 propensity-matched control cohort from the INSERM U970 registry maintained on calcineurin inhibitor, the belatacept group had progressive improvement (P = 0.02) in estimated glomerular filtration rate from a mean of 33.9 ± 10 at baseline to 37.8 ± 13 at 6 months and 38.5 ± 12 mL/min/1.73 m2 at 12 months postconversion, as compared to a steady decline noted in the controls (36.2 [baseline] → 33.1 [6 mo] → 32.7 mL/min/1.73 m2 [12 mo] of follow-up). A paired histologic comparison of preconversion and postconversion (performed at median 9.5 mo postconversion) biopsies showed no worsening in microvascular inflammation or chronicity. The paired tissue gene expression analysis showed improved mean total rejection score (0.68 ± 0.26-0.56 ± 0.33; P = 0.02) and a trend toward improved antibody-mediated rejection score (0.64 ± 0.34-0.56 ± 0.39; P = 0.06). CONCLUSIONS: Here, we report that in patients diagnosed with caAMR who were not subjected to intensive salvage immunosuppressive therapies, isolated belatacept conversion alone was associated with stabilization in renal function. These results are bolstered by molecular evidence of improved inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".