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Record W4387790893 · doi:10.4103/1319-2442.388196

Implications of Banff Classification Schema: A Journey of Three Decades

2022· letter· en· W4387790893 on OpenAlexaboutno aff
Praveen Kumar Etta

Bibliographic record

VenueSaudi Journal of Kidney Diseases and Transplantation · 2022
Typeletter
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSchema (genetic algorithms)Information retrieval

Abstract

fetched live from OpenAlex

To the Editor, The first Banff meeting to standardize descriptions of kidney allograft pathology was held in 1991 and is celebrating three decades of journey now. In the past, several authors have reviewed the evolution of the Banff classification schema, few years ago.[1,2] After these publications, the Banff classification has been modified with each successive meeting, especially with regard to the diagnosis of rejection, both antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR). Here, we have discussed briefly the recent changes in this classification as it has finished three decades since inception. Till the early 1990s, there was considerable heterogeneity among pathologists in the characterization of kidney allograft biopsies. Hence, it was felt that standardization of allo-graft pathology was necessary to allow comparisons of the efficacy of different therapies and to help guide treatment. Initial classification systems that have been introduced include the Banff classification and the Cooperative Clinical Trials in Transplantation (CCTT) classification.[3] Kidney allograft pathology has been standardized with the introduction of Banff classification schema three decades ago, in the year 1991. It represented the first attempt to formulate an international consensus based and structured classification system for the diagnosis and categorization of kidney allograft pathology. In this regard, the first Banff meeting was held at Banff, Alberta, Canada in 1991 and the first publication appeared in 1993 (Banff ’93).[4] The Banff group comprised a group of pathologists, immunologists, physicians, surgeons, and immunogeneticists. Subsequent follow-up meetings have taken place every two years and the Banff schema has undergone considerable evolution over the last three decades. The Banff ’93 and the CCTT systems were both incorporated into the Banff ’97 classification. With its regular updates, the classification has changed with every success-sive meeting. Banff has introduced a numerical grading system for each of the kidney compartments – interstitium (i), tubules (t), vessels (v), and glomeruli (g). The precise histological characterization and differentiation have led to a better understanding of pathogenesis with its therapeutic implications. This has indirectly led to early, accurate diagnosis of graft pathology including rejection, its histological differentiation, differentiating TCMR and ABMR, grading the severity, and determining the degree of irreversible kidney damage [interstitial fibrosis and tubular atrophy (IFTA)], which enabled implementation of preemptive strategies and thereby better long-term graft survival. The presence of linear staining for C4d, a degradation product of the complement pathway that binds covalently to the endothelium, is highly suggestive of ABMR. C4d serves as an immunologic footprint of complement activation and ABMR. It was agreed that C4d staining in at least 10% of peritubular capillaries (C4d2 or C4d3) by immuno-fluorescence (IF) on frozen sections or in any peritubular capillaries by immunoperoxidase on paraffin sections (C4d score >0) should be regarded as “positive.” Some patients have morphologic evidence of ABMR and positive donor-specific antibodies (DSA) with little or no C4d staining. Diagnostic criteria for C4d-negative ABMR were incorporated into the 2013 Banff update.[5] Patients with the evidence of tissue injury and C4d positivity but no evidence of DSAs are typically managed as patients with ABMR.[6] Cases in which C4d staining is positive but DSA cannot be detected may result from DSA being below the level of detection due to immunoadsorption by the graft (sink effect) or it can also be due to the presence of nonhuman leukocyte antigen antibodies. This issue has been particularly discussed in the 2017 Banff conference.[7] Although molecular diagnostics were first introduced into the 2013 Banff, its 2017 update has recommended indications for the use of these tests in kidney allograft biopsy diagnosis. The inflammation in areas of the cortex with IFTA (i-IFTA) is the morphologic correlate of active injury and predicts disease progression. The total cortical inflammation (Banff ti score) was more predictive of graft loss than inflammation in non-sclerotic areas of cortex (Banff i score), indicating the impact of i-IFTA on graft outcomes.[8] The long-term deterioration of kidney allograft function study also showed a strong association between the severity of i-IFTA and graft loss, far stronger than that of IFTA alone.[9] The clinical implications of i-IFTA and its relationship to chronic active (CA) TCMR were well described in 2017 Banff. The alternatives to the DSA criterion were suggested in ABMR diagnosis. C4d and molecular classifiers were recognized as surrogate markers for DSA. The removal of the term “acute” from “acute/active” ABMR was suggested, to mention it simply as “active” ABMR. Tubulitis should be noted both within and outside of scarred areas. It is suggested to score tubulitis independently both in areas of preserved cortex (t score) and within areas of cortical IFTA (t-IFTA score), providing that severely atrophic tubules are not scored. The diagnostic criteria for CA TCMR were modified. In addition to i-IFTA score of ≥2, CA TCMR requires (1) at least a moderate degree of total cortical inflammation (ti score ≥2) and (2) moderate tubulitis involving cortical tubules other than severely atrophic tubules (t or t-IFTA score ≥2). The most recent, XV Banff conference was held in conjunction with the annual meeting of the American Society for Histocompatibility and Immunogenetics in Pittsburgh, USA, in 2019.[10] It focused on refining recent updates to the classification of ABMR, CA TCMR, and borderline (suspicious) for acute TCMR, advances from the Banff working groups, and standardization of molecular diagnostics. This recent update supported 2017 Banff criteria for CA TCMR, but also suggested to indicate additionally, the level of active inflammation in the non-scarred cortex (i score). In cases of CA TCMR associated with i-IFTA, inflammation within non-scarred areas (i score) meeting criteria for additional borderline acute TCMR or acute TCMR Grade IA or IB, should also be specifically reported as a separately reported diagnosis. This also applies to cases where intimal arteritis (v score) is present in addition to CA TCMR. In addition, in the latest Banff update of 2019, a new classification of polyomavirus nephropathy (PVN) was proposed based on two histologic markers predictive for graft survival: the Banff interstitial fibrosis (ci) score and a new score termed “the intrarenal polyomavirus load level (pvl).” The pvl score is based on the fraction of tubules with evidence of polyomavirus replication either by light microscopy or immunohistochemical staining of epithelial cell nuclei for SV40 large T antigen (pvl1 ≤1%, pvl2 >1%, and <10%, pvl3 ≥10%). The pvl and ci scores were used to define 3 PVN classes (Table 1).[10]Table 1.: Updated Banff 2019 classification of kidney allograft pathology.[ 10 ]In conclusion, the updated Banff diagnostic criteria for kidney allograft rejection and related lesions have led to improved diagnostic accuracy and better clinicopathologic correlations. The need for the development of additional diagnostic modalities, including molecular diagnostics will be addressed at the XVI Banff meeting, which will be held in Banff, Canada, celebrating the 30-year anniver-sary at the original location of its inception. Conflict of interest: None declared.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.308
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
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