Liver graft allocation for hepatocellular carcinoma patients: Is a forced ablate-and-wait protocol nationally applicable?
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».