Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/16517
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dc.contributor.authorLau, Lawrence F-
dc.contributor.authorKankanige, Yamuna-
dc.contributor.authorRubinstein, Benjamin-
dc.contributor.authorJones, Robert M-
dc.contributor.authorChristophi, Christopher-
dc.contributor.authorMuralidharan, Vijayaragavan-
dc.contributor.authorBailey, James-
dc.date2016-12-08-
dc.date.accessioned2017-01-16T02:54:19Z-
dc.date.available2017-01-16T02:54:19Z-
dc.date.issued2017-04-
dc.identifier.citationTransplantation 2017; 101(4): e125-e132en_US
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/16517-
dc.description.abstractBACKGROUND: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.en_US
dc.language.isoenen_US
dc.titleMachine-learning algorithms predict graft failure following liver transplantationen_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleTransplantationen_US
dc.identifier.affiliationSurgeryen_US
dc.identifier.affiliationDepartment of Computing and Information Systems, University of Melbourne, Victoria, Australiaen_US
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/27941428en_US
dc.identifier.doi10.1097/TP.0000000000001600en_US
dc.type.contentTexten_US
dc.type.austinJournal Articleen_US
local.name.researcherChristophi, Christopher
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
crisitem.author.deptSurgery (University of Melbourne)-
crisitem.author.deptVictorian Liver Transplant Unit-
crisitem.author.deptSurgery (University of Melbourne)-
crisitem.author.deptHepatopancreatobiliary Surgery-
crisitem.author.deptGastroenterology and Hepatology-
crisitem.author.deptSurgery-
crisitem.author.deptHepatopancreatobiliary Surgery-
crisitem.author.deptSurgery (University of Melbourne)-
crisitem.author.deptHepatopancreatobiliary Surgery-
crisitem.author.deptSurgery-
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