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Enregistrement W2979532806 · doi:10.1182/blood.v128.22.2235.2235

Refined Hepatic Grading System Improves Risk Stratification of Long-Term Outcomes in the Patients Developing Chronic Gvhd

2016· article· en· W2979532806 sur OpenAlexaff
Saud Alhayli, Elizabeth Shin, Wilson Lam, Uday Deotare, Fotios V. Michelis, Santhosh Thyagu, Auro Viswabandya, Jeffrey H. Lipton, Hans A. Messner, Dennis Dong Hwan Kim

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGlycosylation and Glycoproteins Research
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineGrading (engineering)GastroenterologyOverlap syndromeCommon Terminology Criteria for Adverse EventsBilirubinRetrospective cohort studyOncologyAdverse effectDiseaseBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Chronic GVHD (cGVHD) is a syndrome with diverse clinical features resembling autoimmune disorders. cGVHD affects long-term outcomes of allogeneic HCT, resulting in significant morbidity and mortality. Scoring the severity of cGVHD has been proposed with recent changes in the grading system of cGVHD based on the NIH consensus criteria (NCC) in 2015. Grading of liver GVHD is based on the severity of liver enzyme elevation both in NCC in 2005 and 2015. The cutoff of liver enzyme profiles for liver GVHD grading is arbitrarily determined but never been validated. In this study we attempted to evaluate 3 grading systems of hepatic parameters used in 1) NCC 2005, 2) NCC 2015, and 3) the common terminology criteria for adverse events (CTCAE) version 4.0. We also have adopted binary recursive partitioning (rpart) to define the optimal cut-off that provides the best risk stratification to overall survival (OS) after development of cGVHD. Methods: A retrospective review was conducted to compare the hepatic grading systems using liver enzyme parameters used in NCC 2005, NCC2015 and CTCAE v4.0. We reviewed 336 patients who developed cGVHD after allogeneic HCT performed between 2002 and 2014. Long-term outcomes including OS and non-relapse mortality (NRM) after the occurrence of cGVHD were analyzed using the 3 hepatic grading systems. Using rpart, we determined the optimal value for each component of the liver enzyme profile (i.e. AST, ALT, ALP and bilirubin level) was which could identify the best risk stratification of OS. A refined hepatic grading system was generated based on the cut off of ALP and bilirubin level proposed by rpart method, which divided the patients into three groups: low (bilirubin <14 mmol/L and ALP < 146 IU/L), intermediate (bilirubin level ≥14 mmol/L or ALP ≥ 146 IU/L) and high risk (both) . OS and NRM were also compared according to the refined hepatic grading system. Results: Out of 336 patients, 181 had liver involvement of cGVHD. The 3 year OS rate was 74.9% (66.7-81.3%) in the group developing liver GVHD, while that was 67.0% (57.7-74.7%) in the group without liver GVHD (p=0.629). There is no difference of non-relapse mortality (NRM) between patients with or without liver GVHD (14.4% vs. 17.2%; p= 0.661). In the patients developing liver GVHD, 3 hepatic grading systems were evaluated with respect to OS and NRM after onset of cGVHD. None of the 3 grading systems could stratify the patients statistically according to OS (p=0.211 for NCC 2005; p=0.423 for NCC 2015; p=0.461 for CTCAE4.0) or to NRM (p=0.615 for NCC 2005; p=0.327 for NCC 2015; p=0.941 for CTCAE v4.0). Using rpart, we found that 1) bilirubin level ≥14 mmol/L (p=0.01) and 2) ALP ≥ 146 IU/L (p=0.059) are associated with shorter OS, 2) AST and ALT levels were not associated with OS or NRM. A refined hepatic grading system was generated with assignment of a score to each risk factor. A score of 1 was assigned to bilirubin ≥14 mmol/L and ALP ≥ 146 IU/L, each. Total score was calculated with risk score 0 (n=54, 30.0%), risk score 1 (n=85, 57.2%) and risk score 2 (n=41, 22.8%). This hepatic grading system could stratify the patients according to their OS (p=0.015): 89.6% in low vs. 71.8% in intermediate vs. 58.0% in high risk group after onset of cGVHD. Then, we have applied the refined hepatic grading system into all 336 patients developing cGVHD regardless of organ involvement. As expected, the hepatic grading system can stratify 336 patients according to OS: 79.6% in low vs. 65.4% in intermediate vs. 53.9% in high risk group after onset of cGVHD (p=0.001); according to NRM: 11.9% in low vs. 17.2% in intermediate vs. 26.1% in high risk group after onset of cGVHD (p=0.089). Multivariate analysis was performed including 9 covariates including refined hepatic grading system, liver involvement of cGVHD, cGVHD subtype, cGVHD onset < 5 months, age (by decade), platelet counts, HLA match, gender mismatch, and T cell depletion, and confirmed that the refined hepatic grading system is an independent prognostic factor for OS (p=0.003, HR 0.491) in addition to cGVHD onset <5 months and HLA match. Conclusions: None of hepatic grading systems could stratify the patients according to OS/NRM after development of cGVHD. The refined hepatic grading system using bilirubin ≥14 mmol/L and ALP ≥ 146 IU/L at onset of cGVHD defined by the rpart method, could improve risk stratification of the patients developing cGVHD. Disclosures No relevant conflicts of interest to declare.

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)0,002
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,015
Tête enseignante GPT0,271
Écart entre enseignants0,256 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2016
Routes d'admission1
Résumé présentoui

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