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Enregistrement W1678445379 · doi:10.1002/lt.23849

Liver graft allocation for hepatocellular carcinoma patients: Is a forced ablate-and-wait protocol nationally applicable?

2014· letter· en· W1678445379 sur OpenAlexaff
Fateh Bazerbachi, Elizabeth S. Aby, John R. Lake

Notice bibliographique

RevueLiver Transplantation · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueHepatocellular Carcinoma Treatment and Prognosis
Établissements canadiensUniversity Health NetworkUniversity of TorontoToronto General Hospital
Organismes subventionnairesUniversity of Minnesota
Mots-clésMedicineLiver transplantationHepatocellular carcinomaCohortTransplantationMilan criteriaInternal medicineOncologySurgeryIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

We welcome the comments from Mehta et al. regarding our editorial1 and this opportunity to further discuss our opinions. In the original article, Mehta et al.2 identified a specific cohort of hepatocellular carcinoma (HCC) patients with T2 tumors whose risk of wait-list dropout was very low, and they suggested that such patients should not receive the same allocation advantage as higher risk groups (ie, those not meeting their criteria). In our editorial, we raised the issue of whether using such data to inform a new allocation algorithm might lead to worsening outcomes through the selection of recipients with a higher risk of tumor recurrence for liver transplantation (LT). The authors have now provided outcome data to supplement their study and compared the identified outcomes of transplantation for their low-risk subgroup with those for the remaining HCC patients. Although the recurrence rates did not appear different, the non–low-risk recipients had worse 5-year patient survival, with HCC recurrence representing the most common cause of death. We disagree with their conclusion that “excluding patients with a very low risk of wait-list dropout from LT does not mean that we would instead perform transplantation for patients with a high risk of posttransplant HCC recurrence and poor survival,” and here we will focus on 2 primary issues. First, the primary outcome that these investigators should have reported is recurrence-free patient survival for the 2 cohorts. Because 5-year survival was significantly worse for the non–low-risk recipients and the recurrence rates were numerically higher for the non–low-risk recipients, recurrence-free survival will undoubtedly be significantly worse for the non–low-risk recipients. This actually validates our concern. Second, these additional data are relatively unique to the University of California San Francisco (UCSF) for 2 reasons. LT outcomes at UCSF are excellent, as evidenced by the 94% 5-year patient survival rate for the low-risk cohort, and as such, the statement that “rates … for all others compare favorably with published United Network for Organ Sharing data” really has no meaning because it is the equivalent of comparing apples to oranges. More importantly, centers in the donor service area in which UCSF functions perform transplantation with one of the highest (if not the highest) mean Model for End-Stage Liver Disease scores in the country. In the original study, patients at UCSF underwent LT with a median waiting time of 8.8 months (interquartile range = 5.9-12.8 months). During the waiting period, 16.6% dropped out because of tumor progression, and 6.5% died (23.1% in all). The median time from listing to dropout (due to death or tumor progression) was 7.2 months (interquartile range = 3.5-10.6 months). However, a recent liver simulated allocation modeling analysis testing the effects of mandatory waiting times on transplant rates showed that with the current allocation system (before the initiation of Share 35), the mean match Model for End-Stage Liver Disease score for patients undergoing transplantation for HCC in the United States was 24 (unpublished data). This indicates that the majority of patients undergoing transplantation for HCC in the United States do so within 3 months of receiving a Model for End-Stage Liver Disease exception score. In a previous article,3 these authors endorsed the ablate-and-wait principle, which allows the preselection of candidates with a more favorable tumor biology for transplantation. Indeed, the longer waiting periods that characterize the UCSF donor service area provide a de facto forced ablate-and-wait protocol. In such a system, which is not characteristic of the country in general, favorable outcomes are conceivable. In our opinion, the authors' data are relatively unique to their center because of the prolonged average wait-list time for LT candidates and their excellent outcomes. Even so, we believe that these data actually support our premise that blindly using these data to inform allocation policy could lead to worse posttransplant outcomes. We strongly believe that further examinations of outcomes across all regions and donor service areas with any newly proposed allocation system will be necessary for policymaking. Fateh Bazerbachi, M.D.1 Elizabeth Aby, B.A.1 John Lake, M.D.1 1Department of Medicine University of Minnesota Minneapolis, MN

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Protocole · Signal consensuel: aucune
Score de désaccord entre enseignants0,570
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,036
Tête enseignante GPT0,243
Écart entre enseignants0,207 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreProtocole

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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