Liver graft allocation for hepatocellular carcinoma patients: Is a forced ablate-and-wait protocol nationally applicable?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".