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; Sg Corradini; Salvatore Agnes; C Gavrila; M Salizzoni; Ad Pinna; U Cillo; B Gridelli; Lg De Carlis; M Colledan; Ge Gerunda; An Costa; M. Strazzabosco

doi: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

M Angelico
, A Nardi
, R Romagnoli^*
, T Marianelli
, Sg Corradini
, Salvatore Agnes
, C Gavrila
, M Salizzoni
, Ad Pinna
, U Cillo
, B Gridelli
, Lg De Carlis
, M Colledan
, Ge Gerunda
, An Costa
, M. Strazzabosco

^*Autore corrispondente per questo lavoro

Dipartimento di Medicina e chirurgia traslazionale

Risultato della ricerca: Contributo in rivista › Articolo

11 Citazioni (Scopus)

Abstract

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.

Lingua originale	Inglese
pagine (da-a)	N/A-N/A
Rivista	Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Numero di pubblicazione	Gennaio
DOI	https://doi.org/10.1016/j.dld.2013.11.004
Stato di pubblicazione	Pubblicato - 2014

OSS delle Nazioni Unite

Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile

SDG 3 Salute e benessere

All Science Journal Classification (ASJC) codes

Epatologia
Gastroenterologia

Keywords

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

Accesso al documento

10.1016/j.dld.2013.11.004

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Angelico, M., Nardi, A., Romagnoli, R., Marianelli, T., Corradini, S., Agnes, S., Gavrila, C., Salizzoni, M., Pinna, A., Cillo, U., Gridelli, B., De Carlis, L., Colledan, M., Gerunda, G., Costa, A., & 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 : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver, (Gennaio), N/A-N/A. https://doi.org/10.1016/j.dld.2013.11.004

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abstract = "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.",

keywords = "Donor Risk Index, Donor-recipient match, Graft failure, Hepatitis C, Risk factors, Transplantation outcome, Donor Risk Index, Donor-recipient match, Graft failure, Hepatitis C, Risk factors, Transplantation outcome",

author = "M Angelico and A Nardi and R Romagnoli and T Marianelli and Sg Corradini and Salvatore Agnes and C Gavrila and M Salizzoni and Ad Pinna and U Cillo and B Gridelli and \{De Carlis\}, Lg and M Colledan and Ge Gerunda and An Costa and M. Strazzabosco",

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Angelico, M, Nardi, A, Romagnoli, R, Marianelli, T, Corradini, S, Agnes, S, Gavrila, C, Salizzoni, M, Pinna, A, Cillo, U, Gridelli, B, De Carlis, L, Colledan, M, Gerunda, G, Costa, A & 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 : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver, n. Gennaio, pagg. N/A-N/A. 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, M; Nardi, A; Romagnoli, R et al.
In: Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver, N. Gennaio, 2014, pag. N/A-N/A.

Risultato della ricerca: Contributo in rivista › Articolo

TY - JOUR

T1 - A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study

AU - Angelico, M

AU - Nardi, A

AU - Romagnoli, R

AU - Marianelli, T

AU - Corradini, Sg

AU - Agnes, Salvatore

AU - Gavrila, C

AU - Salizzoni, M

AU - Pinna, Ad

AU - Cillo, U

AU - Gridelli, B

AU - De Carlis, Lg

AU - Colledan, M

AU - Gerunda, Ge

AU - Costa, An

AU - Strazzabosco, M.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Donor Risk Index

KW - Donor-recipient match

KW - Graft failure

KW - Hepatitis C

KW - Risk factors

KW - Transplantation outcome

KW - Donor Risk Index

KW - Donor-recipient match

KW - Graft failure

KW - Hepatitis C

KW - Risk factors

KW - Transplantation outcome

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JF - Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

IS - Gennaio

ER -

Angelico M, Nardi A, Romagnoli R, Marianelli T, Corradini S, Agnes S et al. A Bayesian methodology to improve prediction of early graft loss after liver transplantation derived from the Liver Match study. Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver. 2014;(Gennaio):N/A-N/A. doi: 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

Abstract

OSS delle Nazioni Unite

All Science Journal Classification (ASJC) codes

Keywords

Accesso al documento

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