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.

A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study / M. Angelico, A. Nardi, R. Romagnoli, T. Marianelli, S.G. Corradini, F. Tandoi, C. Gavrila, M. Salizzoni, A.D. Pinna, U. Cillo, B. Gridelli, L.G. De Carlis, M. Colledan, G.E. Gerunda, A.N. Costa, M. Strazzabosco, M. Angelico, U. Cillo, S. Fagiuoli, M. Strazzabosco, P. Caraceni, P.L. Toniutto, T.M. Sal-izzoni, R. Romagnoli, G. Bertolotti, D. Patrono, L. DeCarlis, A. Slim, J.M.E. Mangoni, G. Rossi, L. Caccamo, B. Antonelli, V. Mazzaferro, E. Regalia, C. Sposito, M. Colledan, V. Corno, S. Marin, U. Cillo, A. Vitale, E. Gringeri, M. Donataccio, D. Donataccio, U. Baccarani, D. Lorenzin, D. Bitetto, U. Valente, M. Gelli, P. Cupo, G.E. Gerunda, G. Rompianesi, A.D. Pinna, G.L. Grazi, A. Cucchetti, C. Zanfi, A. Risaliti, M.G. Faraci, G. Tisone, A. Anselmo, I. Lenci, D. Sforza, S. Agnes, M. Di Mugno, A.M. Avolio, G.M. Ettorre, L. Miglioresi, G. Vennarecci, P. Berloco, M. Rossi, G. Corradini, A. Molinaro, F. Calise, V. Scuderi, O. Cuomo, C. Migliaccio, L. Lupo, G. Notarnicola, B. Gridelli, R. Volpes, S. LiPetri, G. Zamboni, G. Carbotta, S. Dedola. - 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

G. Rossi;V. Mazzaferro;C. Sposito;
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.
Donor-recipient match; Donor Risk Index; Graft failure; Hepatitis C; Risk factors; Transplantation outcome
Settore MED/18 - Chirurgia Generale
Article (author)
File in questo prodotto:
File Dimensione Formato  
a bayesian methodology.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 945.28 kB
Formato Adobe PDF
945.28 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

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: http://hdl.handle.net/2434/438520
Citazioni
  • ???jsp.display-item.citation.pmc??? 1
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 11
social impact