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Enregistrement W2312599202 · doi:10.1097/tp.0000000000000178

At the End of the Day, Should We Consider Chronic Histological Lesions?

2014· letter· en· W2312599202 sur OpenAlexaboutno aff
Dany Anglicheau, Christophe Legendre

Notice bibliographique

RevueTransplantation · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueRenal Transplantation Outcomes and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineMultivariate analysisTransplantationKidney diseaseKidney transplantationUrologyKidneyBiopsyPathologyInternal medicineSurgery

Résumé

récupéré en direct d'OpenAlex

In this issue, Naesens et al. (1) evaluate the impact on long-term graft survival of various chronic histological lesions obtained from for cause biopsies performed 1 year after transplantation in a large group of approximately 1,200 kidney transplant recipients transplanted between 1991 and 2001. By confronting a detailed histological analysis with actualized Banff scores and high-quality statistical multivariate analyses, this study will undoubtedly contribute to better characterize the complex interplay of various injuries leading to late graft loss. The main novel result is that chronic histological damage is a significant determinant of kidney transplant outcome, independent of specific diagnoses. Of note, previous literature on the causes of graft loss attributed one single cause to each graft loss (2, 3). Naesens et al. hypothesized that the complex history of a transplant is a better predictor of graft outcome than any specific disease in itself. Multivariate Cox proportional hazards analysis clearly illustrated that renal allograft loss, chronic histological damage, and specific disease processes have additive and independent impact on graft outcome. Though graft loss is multifactorial, the Naesens et al.’s study confirms that rejection phenomena are the main contributor of graft loss, with T cell–mediated rejection, changes suggestive of antibody-mediated rejection (ABMR) and transplant glomerulopathy being the prominent features in 52.8% on the last biopsies before graft loss. This is in perfect agreement with the study from the Edmonton group where 64% of graft loss were caused by ABMR (including ABMR, mixed rejections or probable ABMR) (3) and from the Mayo Clinic study where 35.2% of graft losses were attributed to rejection (including ABMR, T cell–mediated rejection, chronic damage associated with recurrent rejections, and transplant glomerulopathy) (2). Of course, one should remind that the Belgian cohort was transplanted in an earlier era, between 1991 and 2001, where not all patients were treated with the current immunosuppressive regimens. The Naesens study further contributes to better characterize the complex interplay of different injuries leading to late graft loss. Their analysis of the last indication biopsy before graft loss revealed that the most biopsies show an ongoing specific disease (69.4%) or chronic scarring likely caused by prior specific diseases (7.6%). Finally, only 6.9% of cases demonstrated chronic damage in the last biopsy before graft loss, without specific disease in the past. These results seem in accordance with the previous studies that show the importance of specific histological diagnoses for graft outcome and reinforce the message that advanced scarring is associated in most cases with an identifiable cause (2, 3). Obviously, the chronic damage observed in a late indication biopsy often in association with a specific disease is not necessarily caused by that specific disease and could also be related to prior (other) specific diseases and even to a nonspecific fibrogenic process (aging, calcineurin inhibitor (CNI) nephrotoxicity). In addition, a specific disease can evolve over time. For instance, a BK virus associated nephropathy can resolve and led to non-specific allograft scarring lesions. Naesens et al. demonstrate that this nonspecific scarring chronic damage is significantly associated with worse outcome. Evaluating the impact of individual elementary lesions in the last biopsy on graft outcome, Naesens et al. found that interstitial infiltrate, interstitial fibrosis / tubular atrophy (IF/TA), chronic glomerular, C4d deposition, and arteriolar hyalinosis (ah) were independently associated with postbiopsy death-censored graft survival. Surprisingly, although the first four lesions were positively associated with bad outcome, ah was associated with better outcome. This association could have been fortuitous if not already reported in the similarly designed DEKAF study, where the diagnosis of “CNI nephrotoxicity” (presumably partly by histology of ah) was associated with better outcome than no CNI nephrotoxicity (4) and in an independent study of the Edmonton group (5). This suggests that the timing of the biopsies, and the study design, affected this result and could be responsible for the discrepancies. Even if it is tempting to speculate that patients with a higher exposure to CNIs may have had a better control of the immune response, this unexpected finding warrants further study. In addition, the Naesens et al.’s study shows that despite the strong association between IF/TA and ah, IF/TA is associated with worse outcome, whereas ah is associated with improved outcome. This suggests that chronic interstitial, chronic tubular, and ah elementary lesions should not been associated in one non-specific diagnosis group, or even in a CNI toxicity group, because it could be tempting.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,051
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,048

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,051
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,004
Communication savante0,0050,012
Science ouverte0,0030,002
Intégrité de la recherche0,0140,016
Charge utile insuffisante (le modèle a refusé de juger)0,0060,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,080
Tête enseignante GPT0,321
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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