A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the liver match study

Dig Liver Dis. 2014 Apr;46(4):340-7. doi: 10.1016/j.dld.2013.11.004. Epub 2014 Jan 9.


Background: To generate a robust predictive model of Early (3 months) Graft Loss after liver transplantation, we used a Bayesian approach to combine evidence from a prospective European cohort (Liver-Match) and the United Network for Organ Sharing registry.

Methods: Liver-Match included 1480 consecutive primary liver transplants performed from 2007 to 2009 and the United Network for Organ Sharing a time-matched series of 9740 transplants. There were 173 and 706 Early Graft Loss, respectively. Multivariate analysis identified as significant predictors of Early Graft Loss: donor age, donation after cardiac death, cold ischaemia time, donor body mass index and height, recipient creatinine, bilirubin, disease aetiology, prior upper abdominal surgery and portal thrombosis.

Results: A Bayesian Cox model was fitted to Liver-Match data using the United Network for Organ Sharing findings as prior information, allowing to generate an Early Graft Loss-Donor Risk Index and an Early Graft Loss-Recipient Risk Index. A Donor-Recipient Allocation Model, obtained by adding Early Graft Loss-Donor Risk Index to Early Graft Loss-Recipient Risk Index, was then validated in a distinct United Network for Organ Sharing (year 2010) cohort including 2964 transplants. Donor-Recipient Allocation Model updating using the independent Turin Transplant Centre dataset, allowed to predict Early Graft Loss with good accuracy (c-statistic: 0.76).

Conclusion: Donor-Recipient Allocation Model allows a reliable donor and recipient-based Early Graft Loss prediction. The Bayesian approach permits to adapt the original Donor-Recipient Allocation Model by incorporating evidence from other cohorts, resulting in significantly improved predictive capability.

Keywords: Donor Risk Index; Donor-recipient match; Graft failure; Hepatitis C; Risk factors; Transplantation outcome.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Bayes Theorem
  • Body Mass Index
  • Cohort Studies
  • Cold Ischemia / statistics & numerical data
  • Delayed Graft Function / epidemiology
  • End Stage Liver Disease / surgery*
  • Female
  • Graft Rejection / epidemiology
  • Graft Survival*
  • Humans
  • Italy / epidemiology
  • Liver Transplantation / statistics & numerical data*
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Primary Graft Dysfunction / epidemiology
  • Proportional Hazards Models
  • Prospective Studies
  • Risk Assessment*
  • Risk Factors
  • Tissue Donors / statistics & numerical data*
  • Treatment Outcome