Literature DB >> 29485514

Predicting Liver Allograft Discard: The Discard Risk Index.

Abbas Rana1, Rohini R Sigireddi1, Karim J Halazun2, Aishwarya Kothare1, Meng-Fen Wu3, Hao Liu3, Michael L Kueht1, John M Vierling1, Norman L Sussman1, Ayse L Mindikoglu1, Tamir Miloh4,5, N Thao N Galvan1, Ronald T Cotton1, Christine A O'Mahony1, John A Goss1.   

Abstract

BACKGROUND: An index that predicts liver allograft discard can effectively grade allografts and can be used to preferentially allocate marginal allografts to aggressive centers. The aim of this study is to devise an index to predict liver allograft discard using only risk factors available at the time of initial DonorNet offer.
METHODS: Using univariate and multivariate analyses on a training set of 72 297 deceased donors, we identified independent risk factors for liver allograft discard. Multiple imputation was used to account for missing variables.
RESULTS: We identified 15 factors as significant predictors of liver allograft discard; the most significant risk factors were: total bilirubin > 10 mg/dL (odds ratio [OR], 25.23; confidence interval [CI], 17.32-36.77), donation after circulatory death (OR, 14.13; CI, 13.30-15.01), and total bilirubin 5 to 10 mg/dL (OR, 7.57; 95% CI, 6.32-9.05). The resulting Discard Risk Index (DSRI) accurately predicted the risk of liver discard with a C statistic of 0.80. We internally validated the model with a validation set of 37 243 deceased donors and also achieved a 0.80 C statistic. At a DSRI at the 90th percentile, the discard rate was 50% (OR, 32.34; CI, 28.63-36.53), whereas at a DSRI at 10th percentile, only 3% of livers were discarded.
CONCLUSIONS: The use of the DSRI can help predict liver allograft discard. The DSRI can be used to effectively grade allografts and preferentially allocate marginal allografts to aggressive centers to maximize the donor yield and expedite allocation.

Entities:  

Mesh:

Year:  2018        PMID: 29485514     DOI: 10.1097/TP.0000000000002151

Source DB:  PubMed          Journal:  Transplantation        ISSN: 0041-1337            Impact factor:   4.939


  3 in total

1.  Predictive Capacity of Risk Models in Liver Transplantation.

Authors:  Jacob D de Boer; Hein Putter; Joris J Blok; Ian P J Alwayn; Bart van Hoek; Andries E Braat
Journal:  Transplant Direct       Date:  2019-05-22

2.  Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.

Authors:  Andrew M Bishara; Dmytro S Lituiev; Dieter Adelmann; Rishi P Kothari; Darren J Malinoski; Jacob D Nudel; Mitchell B Sally; Ryutaro Hirose; Dexter D Hadley; Claus U Niemann
Journal:  Transplant Direct       Date:  2021-09-27

3.  Trends in Outcomes for Marginal Allografts in Liver Transplant.

Authors:  Theodore Zhang; Jordan Dunson; Fasiha Kanwal; Nhu Thao Nguyen Galvan; John M Vierling; Christine O'Mahony; John A Goss; Abbas Rana
Journal:  JAMA Surg       Date:  2020-08-05       Impact factor: 14.766

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.