Liver allocation for hepatocellular carcinoma: a European Center policy in the pre-MELD era

Transplantation. 2006 Feb 27;81(4):525-30. doi: 10.1097/01.tp.0000198741.39637.44.

Abstract

Background: Policies to decrease dropout during waiting time for liver transplantation (LT) are under debate.

Methods: We evaluated the allocation system from 1996 to 2003, when recipients had priority related to Child-Pugh score and donors >60 years were mainly offered to recipients with hepatocellular carcinoma (HCC). The outcomes of 656 patients with chronic liver disease (142 HCC and 514 non-HCC) listed for LT were prospectively evaluated, considering recipient and donor features.

Results: Transplantation and dropout rates were similar between HCC and non-HCC patients: 64.1% vs. 70.6% and 26% vs. 22.6%. Multivariate analysis showed the probability of being transplanted within 3 months was related to Child-Pugh score >10 and to HCC, whereas the probability of being removed from the list within 3 months was only related to Child-Pugh score >10. HCC patients had a lower median waiting time (97 vs. 197 days, P<0.001), a higher rate of donors > 60 years (50.5% vs. 33.5%, P<0.005) and with steatosis (31.6% vs. 14.3%, P<0.01), but a lower Child-Pugh score (9.1+/-2.1 vs. 9.6+/-1.7, P<0.05) than non-HCC patients. The 5-year patient survival was comparable since registration on the list and since LT: 56.9% and 77% in the HCC group vs. 61.4% and 79% in the non-HCC patients. Donors > 60 years affected outcome after LT in the non-HCC group, but not in the HCC patients.

Conclusion: By allocating donors >60 years mainly to HCC patients, we controlled dropout without affecting their survival and the outcome of non-HCC patients.

Publication types

  • Multicenter Study

MeSH terms

  • Aged
  • Carcinoma, Hepatocellular / surgery*
  • Europe
  • Health Care Rationing
  • Health Policy
  • Humans
  • Liver Diseases / surgery*
  • Liver Neoplasms / surgery*
  • Liver Transplantation / mortality
  • Liver Transplantation / statistics & numerical data*
  • Middle Aged
  • Patient Dropouts
  • Postoperative Complications / classification
  • Survival Analysis
  • Tissue Donors / statistics & numerical data
  • Tissue and Organ Procurement / statistics & numerical data*
  • Treatment Outcome