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, Stefano Ginanni Corradini, 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

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)340-347
Number of pages8
JournalDigestive and Liver Disease
Volume46
Issue number4
DOIs
Publication statusPublished - 2014

Fingerprint

Liver Transplantation
Transplants
Liver
Bayes Theorem
Cold Ischemia
Bilirubin
Proportional Hazards Models
Registries
Creatinine
Body Mass Index
Thrombosis
Multivariate Analysis

Keywords

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

ASJC Scopus subject areas

  • Gastroenterology
  • Hepatology

Cite this

Angelico, M., Nardi, A., Romagnoli, R., Marianelli, T., Corradini, S. G., Tandoi, F., ... Strazzabosco, M. (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. https://doi.org/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. / Angelico, Mario; Nardi, Alessandra; Romagnoli, Renato; Marianelli, Tania; Corradini, Stefano Ginanni; Tandoi, Francesco; Gavrila, Caius; Salizzoni, Mauro; Pinna, Antonio D.; Cillo, Umberto; Gridelli, Bruno; De Carlis, Luciano G.; Colledan, Michele; Gerunda, Giorgio E.; Costa, Alessandro Nanni; Strazzabosco, Mario.

In: Digestive and Liver Disease, Vol. 46, No. 4, 2014, p. 340-347.

Research output: Contribution to journalArticle

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 2014, 'A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study', Digestive and Liver Disease, vol. 46, no. 4, pp. 340-347. https://doi.org/10.1016/j.dld.2013.11.004
Angelico, Mario ; Nardi, Alessandra ; Romagnoli, Renato ; Marianelli, Tania ; Corradini, Stefano Ginanni ; Tandoi, Francesco ; Gavrila, Caius ; Salizzoni, Mauro ; Pinna, Antonio D. ; Cillo, Umberto ; Gridelli, Bruno ; De Carlis, Luciano G. ; Colledan, Michele ; Gerunda, Giorgio E. ; Costa, Alessandro Nanni ; Strazzabosco, Mario. / A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study. In: Digestive and Liver Disease. 2014 ; Vol. 46, No. 4. pp. 340-347.
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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.",
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AU - Angelico, Mario

AU - Nardi, Alessandra

AU - Romagnoli, Renato

AU - Marianelli, Tania

AU - Corradini, Stefano Ginanni

AU - Tandoi, Francesco

AU - Gavrila, Caius

AU - Salizzoni, Mauro

AU - Pinna, Antonio D.

AU - Cillo, Umberto

AU - Gridelli, Bruno

AU - De Carlis, Luciano G.

AU - Colledan, Michele

AU - Gerunda, Giorgio E.

AU - Costa, Alessandro Nanni

AU - Strazzabosco, Mario

PY - 2014

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N2 - 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.

AB - 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.

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KW - Hepatitis C

KW - Risk factors

KW - Transplantation outcome

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