Multi-HCC: A practical model to prioritize patients with hepatocellular carcinoma on the liver transplant waiting list.

Abstract

Currently, patients with hepatocellular carcinoma (HCC) in the United States are assigned a uniform score relative to the median MELD at transplant (MMaT-3) after a minimum 6-month waiting period. Here, we develop a risk stratification model for patients with HCC, using the available and objective variables at time of listing.We identified adult liver transplant candidates with approved HCC exception in the OPTN database from 2015-2022. 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 3.0.18,273 patients with HCC were listed for liver transplant with a median MELD 3.0 of 11 (IQR 8-15) and AFP 6 (IQR 4-17). Since all models performed similarly, a parsimonious Cox-based model comprised of MELD 3.0, AFP, and tumor burden, Multi-HCC (Model for 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, deMELD, and MELD-Eq.An urgency-based priority system for patients with HCC, similar to MELD for patients with chronic liver disease, is achievable with a parsimonious model incorporating AFP, 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.

View details for DOI 10.1053/j.gastro.2024.11.015

View details for PubMedID 39622302