Graft Survival Following Microvascular Inflammation: Effect of Donor-Specific Antibodies and Cross-Match Status in Kidney Transplant Recipients
Bibliographic record
Abstract
Background: Microvascular inflammation (MVI) is a hallmark of antibody-mediated rejection (ABMR) and is linked to variable graft outcomes. The Banff 2022 classification recognizes MVI as a spectrum based on the presence or absence of donor-specific antibodies (DSA). We evaluated how DSA status and flow cytometric crossmatch (XM) affect graft survival in kidney transplant recipients with MVI. Methods: We conducted a retrospective cohort study of kidney transplant recipients at Mayo Clinic Rochester (2010–2024) with at least one biopsy showing MVI. Indication and surveillance biopsies (4 months, 1-, 2-, 4-, and 7-yrs post-transplant) were included. Patients were categorized into four groups: de novo DSA, preformed DSA with positive or negative XM, and no DSA. DSA was defined using MFI >1000, and de novo DSA was newly detected 3 months post-transplant. Graft survival was assessed using Kaplan-Meier methods. Results: Among 3,352 recipients, 410 (12%) had MVI with DSA data: 19% de novo DSA, 62% preexisting DSA, and 19% no DSA. Among DSA-positive patients, 72% had a positive XM. Mean follow-up was 4.5±3.2 years. Graft survival differed significantly by DSA group (p = 0.031), with the best outcomes in DSA-negative patients. Survival was similar in de novo and preformed DSA groups. Among DSA-positive recipients, those with a positive XM trended toward worse survival than those with a negative XM (p = 0.062). Conclusion: In kidney transplant recipients with MVI, DSA and XM status predict graft survival. Absence of DSA was linked to better outcomes, while a positive XM identified a higher-risk group. Graft survival was similar in patients with de novo and preformed DSA, differing from prior studies—possibly due to the inclusion of surveillance biopsies, which may have enabled earlier detection. These markers can guide risk stratification and personalize post-transplant care.
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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 | 0.000 | 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) | 0.001 | 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".