A Multicenter Application of the 2018 Banff Classification for BK Polyomavirus-associated Nephropathy in Renal Transplantation
Bibliographic record
Abstract
BACKGROUND: With current immunosuppressive regimens, BK polyomavirus-associated nephropathy (BKPyVAN) is still a matter of concern. Stratification of patients at risk for allograft loss is of uttermost importance to guide treatment choice and assess prognosis. In 2018, the Banff working group proposed a classification scheme for the prognosis of BKPyVAN, but external application on independent cohorts is yet to be performed. We investigated how the 2018 Banff classification would perform in a multicenter cohort comprising 50 cases of biopsy-proven BKPyVAN compared to previously published classification systems. METHODS: We analyzed consecutive BKPyVAN cases from two Dutch university hospitals between 2002 and 2013, retrieved clinical data, and scored all biopsies according to the Banff 2018 classification, and as a comparison, 4 previously proposed BKPyVAN classification systems. We used estimated glomerular filtration rate trajectories and death-censored graft survival as primary endpoints. RESULTS: The 2018 Banff classification did not associate with estimated glomerular filtration rate decline or graft failure and performed only slightly better than the 4 previously proposed classifiers. Anti-human leukocyte antigen donor-specific antibodies (DSAs), especially in combination with ongoing biopsy-proven BKPyVAN on follow-up, did correlate with graft function and survival. Patients who were DSA+/BKPyVAN+ on follow-up had more inflammation at the baseline biopsy, which by itself was not associated with graft outcomes. CONCLUSIONS: Neither the 2018 Banff BKPyVAN classification nor previously published stratification systems could be applied to our multicenter patient cohort. Our data suggest that there might be a prognostic value for follow-up biopsies and DSA measurements to improve risk stratification after BKPyVAN, although prospective multicenter efforts with protocol measurements are needed to confirm this.
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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) | 0.000 | 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) | 0.000 | 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".