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Dig Liver Dis. 2014 Apr;46(4):340-7. doi: 10.1016/j.dld.2013.11.004. Epub 2014 Jan 9.

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

Author information

1
Liver Unit, Tor Vergata University, Rome, Italy; Italian Association for the Study of the Liver (AISF), Italian National Transplant Centre (CNT) and Italian Liver Transplant Centres, Italy.
2
Department of Mathematics, Tor Vergata University, Rome, Italy.
3
Liver Transplant Unit, Azienda Ospedaliera Città della Salute e della Scienza, University of Turin, Italy; Italian Association for the Study of the Liver (AISF), Italian National Transplant Centre (CNT) and Italian Liver Transplant Centres, Italy. Electronic address: reromag@tin.it.
4
Gastroenterology Unit, La Sapienza University, Rome, Italy; Italian Association for the Study of the Liver (AISF), Italian National Transplant Centre (CNT) and Italian Liver Transplant Centres, Italy.
5
Liver Transplant Unit, Azienda Ospedaliera Città della Salute e della Scienza, University of Turin, Italy.
6
Liver Transplant Unit, University of Bologna, Italy.
7
Liver Transplant Unit, Università of Padua, Italy; Italian Association for the Study of the Liver (AISF), Italian National Transplant Centre (CNT) and Italian Liver Transplant Centres, Italy.
8
Centro Trapianti di Fegato, ISMETT, Palermo, Italy.
9
Liver Transplant Unit, Niguarda Hospital, Milan, Italy.
10
Centro Trapianti di Fegato, Ospedali Riuniti, Bergamo, Italy.
11
Liver Transplant Unit, Università of Modena, Italy.
12
National Transplant Centre, Rome, Italy.
13
Digestive Disease Section, University of Milan Bicocca, Milan, Italy; Yale University Liver Centre, New Haven, USA; Italian Association for the Study of the Liver (AISF), Italian National Transplant Centre (CNT) and Italian Liver Transplant Centres, Italy.

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.

KEYWORDS:

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

PMID:
24411484
DOI:
10.1016/j.dld.2013.11.004
[Indexed for MEDLINE]
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