Identifying the Specific Causes and the Determinants of Outcome in Kidney Recipients with Transplant Glomerulopathy
Bibliographic record
Abstract
Background Understanding the specific causes of TG and its long-term consequences at population scale is lacking. Methods This study includes all kidney allograft biopsies performed between January 2004 and January 2014 in three French referral centers and one Canadian center showing TG (Banff cg score≥1 by light microscopy). All TG cases were extensively phenotyped and systematically assessed using light microscopy, immunohistochemistry (IH), immunofluorescence (IF), together with circulating anti-HLA-DSA at the time of biopsy. Results Among the 8,207 post-transplant allograft biopsies performed during the inclusion period, 552 (6.7%) presented with double contours and corresponded to 385 patients. Three predominant overlapping etiologies accounted for 466 (84.4%) cases. 417 biopsies showed antibody-mediated injury (75.5%), 90 biopsies showed TMA (16.3%), 65 showed MPGN (11.8%), while 86 (15.6%) remained equivocal with no specific cause identified (Figure 1). The median time of TG diagnosis post-transplant was 33.18 months (IQR: 12.12 – 78.72 months). Kidney allograft survival after TG diagnosis was 69.4% at 3 years, 57.1% at 5 years, 43.3% at 7 years and 25.5% at 10 years (Figure2). After adjusting for donor, recipient and transplant characteristics, immunological and histological parameters, we identified the following independent factors associated with long-term allograft survival in patients with TG: eGFR (HR: 0.96; CI 95% (0.95-0.98); p<0.001) and proteinuria level (squared transformation) (HR: 2.16; CI 95% (1.76-2.65); p<0.0001) at the time of biopsy, deceased donor (HR: 1.62; CI 95% (1.11-2.40); p=0.016), delay between transplantation and TG diagnosis (log transformation) (HR:1.31; CI 95% (1.16-1.49); p<0.001), vasculitis Banff score (HR: 1.66; IC 95% (1.04-2.65); p=0.032) and TG biopsy with ongoing disease process (HR: 1.55; IC 95% (1.01-2.391); p=0.047). Conclusion Using a large cohort of kidney recipients with a diagnosis of TG and a systematic phenotyping, we identify three overlapping pathways in TG: ABMR, TMA and MPGN. The identification of the main independent determinants of TG prognosis may help improving risk stratification and define specific causes and disease process in patients with TG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".