Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/16517
Title: Machine-learning algorithms predict graft failure following liver transplantation
Authors: Lau, Lawrence F;Kankanige, Yamuna;Rubinstein, Benjamin;Jones, Robert M;Christophi, Christopher;Muralidharan, Vijayaragavan;Bailey, James
Issue Date: Apr-2017
EDate: 2016-12-08
Citation: Transplantation 2017; 101(4): e125-e132
Abstract: BACKGROUND:Ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritized. An index that is derived to predict graft failure using donor and recipient factors, based on local datasets, will be more beneficial in the Australian context. METHODS:Liver transplant data from the Austin Hospital, Melbourne, Australia, from 2010-2013 has been included in the study. The top 15 donor, recipient and transplant factors influencing the outcome of graft failure within 30 days, were selected using a machine learning methodology. An algorithm predicting the outcome of interest was developed using those factors.RESULTS: Donor Risk Index (DRI) predicts the outcome with an area under the receiver operating characteristic curve (AUC-ROC) value of 0.680 (95% CI 0.669-0.690). The combination of the factors used in DRI with the model for end-stage liver disease (MELD) score yields an AUC-ROC of 0.764 (95% CI 0.756-0.771), whereas Survival outcomes following liver transplantation (SOFT) score obtains an AUC-ROC of 0.638 (95% CI 0.632- 0.645). The top 15 donor and recipient characteristics within random forests results in an AUC-ROC of 0.818 (95% CI 0.812-0.824).CONCLUSIONS:Using donor, transplant and recipient characteristics known at the decision time of a transplant, high accuracy in matching donors and recipients can be achieved, potentially providing assistance with clinical decision making.
URI: http://ahro.austin.org.au/austinjspui/handle/1/16517
DOI: 10.1097/TP.0000000000001600
PubMed URL: https://www.ncbi.nlm.nih.gov/pubmed/27941428
Type: Journal Article
Appears in Collections:Journal articles

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