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.
|Data di pubblicazione:||2014|
|Titolo:||A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the liver match study.|
|Autori:||Angelico, M; Nardi, A; Romagnoli, R; Marianelli, T; Corradini, Sg; Tandoi, F; Gavrila, C; Salizzoni, M; Pinna, Ad; Cillo, U; Gridelli, B; De Carlis, Lg; Colledan, M; Gerunda, Ge; Costa, AN; Strazzabosco, M; Liver, Match Study Investigators|
|Digital Object Identifier (DOI):||10.1016/j.dld.2013.11.004. Epub 2014 Jan 9.|
|Appare nelle tipologie:||Articolo su rivista|
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