Antibody-Mediated Rejection Due to Preexisting versus De Novo Donor-Specific Antibodies in Kidney Allograft Recipients
Bibliographic record
Abstract
Antibody-mediated rejection (ABMR) can occur in patients with preexisting anti-HLA donor-specific antibodies (DSA) or in patients who develop de novo DSA. However, how these processes compare in terms of allograft injury and outcome has not been addressed. From a cohort of 771 kidney biopsy specimens from two North American and five European centers, we performed a systematic assessment of clinical and biologic parameters, histopathology, circulating DSA, and allograft gene expression for all patients with ABMR ( n =205). Overall, 103 (50%) patients had preexisting DSA and 102 (50%) had de novo DSA. Compared with patients with preexisting DSA ABMR, patients with de novo DSA ABMR displayed increased proteinuria, more transplant glomerulopathy lesions, and lower glomerulitis, but similar levels of peritubular capillaritis and C4d deposition. De novo DSA ABMR was characterized by increased expression of IFN γ -inducible, natural killer cell, and T cell transcripts, but less expression of AKI transcripts compared with preexisting DSA ABMR. The preexisting DSA ABMR had superior graft survival compared with the de novo DSA ABMR (63% versus 34% at 8 years after rejection, respectively; P <0.001). After adjusting for clinical, histologic, and immunologic characteristics and treatment, we identified de novo DSA ABMR (hazard ratio [HR], 1.82 compared with preexisting DSA ABMR; 95% confidence interval [95% CI], 1.07 to 3.08; P =0.03); low eGFR (<30 ml/min per 1.73 m 2 ) at diagnosis (HR, 3.27; 95% CI, 1.48 to 7.23; P <0.001); ≥0.30 g/g urine protein-to-creatinine ratio (HR, 2.44; 95% CI, 1.47 to 4.09; P <0.001); and presence of cg lesions (HR, 2.25; 95% CI, 1.34 to 3.79; P =0.002) as the main independent determinants of allograft loss. Our findings support the transplant of kidneys into highly sensitized patients and should encourage efforts to monitor patients for de novo DSA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".