ArticleGastroenterology2025
Model of Urgency for Liver Transplantation in Hepatocellular Carcinoma: A Practical Model to Prioritize Patients With Hepatocellular Carcinoma on the Liver Transplant Waitlist.
Article in Gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Regarding: enhancing the BFE-HCC score model: perspectives on methodological advancement and clinical translation.Annals of medicine · 2026Article
- Emerging applications of traditional and generative artificial intelligence in liver transplantation.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Optimizing Liver Transplant Allocation for Hepatocellular Carcinoma: Development and Validation of a Survival Benefit-Based Model.Clinical transplantation · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Artificial intelligence in liver transplantation: Opportunities and challenges.World journal of gastrointestinal surgery · 2025Review
- [Novel research horizon for liver transplantation management model in hepatocellular carcinoma based on the tumor-donor liver-recipient].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
BACKGROUND &
aimsCurrently, patients with hepatocellular carcinoma (HCC) in the United States are assigned a uniform score relative to the median Model for End-Stage Liver Disease (MELD) at transplant after a minimum 6-month waiting period. The authors developed a risk stratification model for patients with HCC using the available and objective variables at time of listing.
methodsAdult liver transplant candidates with approved HCC exception in the Organ Procurement and Transplantation Network database from 2015-2022 were identified. Cox regression analysis, as well as machine learning models (random survival forest and neural network), were used to develop models predicting waitlist dropout. Predicted waitlist dropout for patients with HCC was scaled to patients without exception using MELD 3.0.
resultsThere were 18,273 patients with HCC listed for liver transplant with a median MELD 3.0 of 11 (interquartile range, 8-15) and α-fetoprotein of 6 ng/mL (interquartile range, 4-17 ng/mL). Because all models performed similarly, a parsimonious Cox-based model composed of MELD 3.0, α-fetoprotein, tumor burden, and Model of Urgency for Liver Transplantation in HCC, was selected, with a C-statistic of 0.71 (95% CI, 0.69-0.74) for 6-month dropout in the validation set, outperforming previous models, including HALT-HCC (Hazard Associated with Liver Transplantation for HCC), deMELD (Dropout Equivalent MELD), and MELD-Eq (MELD Equivalent).
conclusionsAn urgency-based priority system for patients with HCC, similar to MELD for patients with chronic liver disease, is achievable with a parsimonious model incorporating α-fetoprotein, MELD 3.0, and tumor size. This approach can be applied to the liver allocation system to prioritize patients with HCC and can inform decision making regarding urgency weights for exception cases in the upcoming continuous distribution system.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.