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. © 2013 Editrice Gastroenterologica Italiana S.r.l.
A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study / Angelico, M.; Nardi, A.; Romagnoli, R.; Marianelli, T.; Corradini, S. G.; Tandoi, F.; Gavrila, C.; Salizzoni, M.; Pinna, A. D.; Cillo, U.; Gridelli, B.; De Carlis, L. G.; Colledan, M.; Gerunda, G. E.; Costa, A. N.; Strazzabosco, M.; Angelico, M.; Cillo, U.; Fagiuoli, S.; Strazzabosco, M.; Caraceni, P.; Toniutto, P. L.; Sal-izzoni, T. M.; Romagnoli, R.; Bertolotti, G.; Patrono, D.; Decarlis, L.; Slim, A.; Mangoni, J. M. E.; Rossi, G.; Caccamo, L.; Antonelli, B.; Mazzaferro, V.; Regalia, E.; Sposito, C.; Colledan, M.; Corno, V.; Marin, S.; Cillo, U.; Vitale, A.; Gringeri, E.; Donataccio, M.; Donataccio, D.; Baccarani, U.; Lorenzin, D.; Bitetto, D.; Valente, U.; Gelli, M.; Cupo, P.; Gerunda, G. E.; Rompianesi, G.; Pinna, A. D.; Grazi, G. L.; Cucchetti, A.; Zanfi, C.; Risaliti, A.; Faraci, M. G.; Tisone, G.; Anselmo, A.; Lenci, I.; Sforza, D.; Agnes, S.; Di Mugno, M.; Avolio, A. M.; Ettorre, G. M.; Miglioresi, L.; Vennarecci, G.; Berloco, P.; Rossi, M.; Corradini, G.; Molinaro, A.; Calise, F.; Scuderi, V.; Cuomo, O.; Migliaccio, C.; Lupo, L.; Notarnicola, G.; Gridelli, B.; Volpes, R.; Lipetri, S.; Zamboni, G.; Carbotta, G.; Dedola, S.. - In: DIGESTIVE AND LIVER DISEASE. - ISSN 1590-8658. - 46:4(2014), pp. 340-347. [10.1016/j.dld.2013.11.004]
A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study
Gelli M.;Cupo P.;Rompianesi G.;Avolio A. M.;Cuomo O.;Notarnicola G.;
2014
Abstract
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. © 2013 Editrice Gastroenterologica Italiana S.r.l.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
