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. (C) 2013 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study / Mario, Angelico; Alessandra, Nardi; Renato, Romagnoli; Tania, Marianelli; GINANNI CORRADINI, Stefano; 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; Rossi, Massimo. - In: DIGESTIVE AND LIVER DISEASE. - ISSN 1590-8658. - STAMPA. - 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

GINANNI CORRADINI, Stefano;ROSSI, MASSIMO
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. (C) 2013 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
hepatitis c; donor risk index; donor-recipient match; graft failure; transplantation outcome; risk factors
01 Pubblicazione su rivista::01a Articolo in rivista
A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study / Mario, Angelico; Alessandra, Nardi; Renato, Romagnoli; Tania, Marianelli; GINANNI CORRADINI, Stefano; 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; Rossi, Massimo. - In: DIGESTIVE AND LIVER DISEASE. - ISSN 1590-8658. - STAMPA. - 46:4(2014), pp. 340-347. [10.1016/j.dld.2013.11.004]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/541887
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