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

Salvatore Agnes, Mario Angelico, Alessandra Nardi, Renato Romagnoli, Tania Marianelli, Stefano Ginanni Corradini, Francesco Tandoi, Caius Gavrila, Mauro Salizzoni, Antonio D. Pinna, Umberto Cillo, Bruno Gridelli, Luciano G. De Carlis, Michele Colledan, Giorgio E. Gerunda, Alessandro Nanni Costa, Mario Strazzabosco, M. Angelico, U. Cillo, S. FagiuoliM. Strazzabosco, P. Caraceni, P. L. Toniutto, Torino M. Sal-Izzoni, R. Romagnoli, G. Bertolotti, D. Patrono, L. Decarlis, A. Slim, J. M.E. Mangoni, G. Rossi, L. Caccamo, B. Antonelli, V. Mazzaferro, E. Regalia, C. Sposito, M. Colledan, V. Corno

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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.

Keywords

  • Donor Risk Index
  • Donor-recipient match
  • Graft failure
  • Hepatitis C
  • Risk factors
  • Transplantation outcome

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