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.

Angelico, M., Nardi, A., Romagnoli, R., Marianelli, T., Corradini, S., Tandoi, F., et al. (2014). A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study. DIGESTIVE AND LIVER DISEASE, 46(4), 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

DE CARLIS, LUCIANO GREGORIO;Colledan, M;STRAZZABOSCO, MARIO;FAGIUOLI, STEFANO
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.
Articolo in rivista - Articolo scientifico
Donor Risk Index; Donor-recipient match; Graft failure; Hepatitis C; Risk factors; Transplantation outcome;
English
2014
46
4
340
347
none
Angelico, M., Nardi, A., Romagnoli, R., Marianelli, T., Corradini, S., Tandoi, F., et al. (2014). A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study. DIGESTIVE AND LIVER DISEASE, 46(4), 340-347 [10.1016/j.dld.2013.11.004].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/53040
Citazioni
  • Scopus 16
  • ???jsp.display-item.citation.isi??? 14
Social impact