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Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation

Lau, Lawrence MBBS, FRACS; Kankanige, Yamuna BScEng; Rubinstein, Benjamin PhD; Jones, Robert MBChB, FRACS; Christophi, Christopher MD, FRACS; Muralidharan, Vijayaragavan PhD, FRACS; Bailey, James PhD

doi: 10.1097/TP.0000000000001600
Original Clinical Science—Liver

Background The 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 data sets, will be more beneficial in the Australian context.

Methods Liver transplant data from the Austin Hospital, Melbourne, Australia, from 2010 to 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 predicts the outcome with an area under the receiver operating characteristic curve (AUC-ROC) value of 0.680 (95% confidence interval [CI], 0.669-0.690). The combination of the factors used in Donor Risk Index with the model for end-stage liver disease score yields an AUC-ROC of 0.764 (95% CI, 0.756-0.771), whereas survival outcomes after liver transplantation 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.

The authors report an algorithm based on 15 of the top-ranking donor and recipient variables available prior to transplantation for predicting outcome following liver transplantation using a random forest machine learning. Supplemental digital content is available in the text.

1 Department of Surgery, Austin Hospital, Heidelberg, Melbourne, Australia.

2 Department of Computing and Information Systems, University of Melbourne, Australia.

Received 1 June 2016. Revision received 13 October 2016.

Accepted 21 November 2016.

L.L. and Y.K. are joint first authors.

V.M. and J.B. are joint last authors.

The authors were supported in part by the Royal Australasian College of Surgeons Surgeon Scientist Research Scholarship, the Avant Doctor in Training Research Scholarship and the Australian Postgraduate Award.

The authors declare no conflicts of interest.

L.L. participated in research design, data collection, and article writing. Y.K. participated in research design, data analysis, and article writing. B.R. participated in research design, data analysis, and article revision. R.J. participated in research design and article revision. C.C. participated in research design and article revision. V.M. participated in research design and article revision. J.B. participated in research design, data analysis, and article revision.

Correspondence: Lawrence Lau, MBBS, FRACS, Department of Surgery, Austin Hospital Heidelberg, Melbourne, Australia. (

Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (

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