title: Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.
abstract: 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.
authors.affiliation: 1 Department of Surgery, Austin Hospital, Heidelberg, Melbourne, Australia. 2 Department of Computing and Information Systems, University of Melbourne, Australia.
authors.forename: Lawrence
authors.initials: L
authors.lastname: Lau
authors.forename: Yamuna
authors.initials: Y
authors.lastname: Kankanige
authors.forename: Benjamin
authors.initials: B
authors.lastname: Rubinstein
authors.forename: Robert
authors.initials: R
authors.lastname: Jones
authors.forename: Christopher
authors.initials: C
authors.lastname: Christophi
authors.forename: Vijayaragavan
authors.initials: V
authors.lastname: Muralidharan
authors.forename: James
authors.initials: J
authors.lastname: Bailey
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D016208:Databases, Factual; D003661:Decision Support Techniques; D046148:Donor Selection; D005260:Female; D006085:Graft Survival; D006801:Humans; D016031:Liver Transplantation / Q000009:adverse effects* / Q000401:mortality; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D011237:Predictive Value of Tests; D012372:ROC Curve; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D014019:Tissue Donors / Q000600:supply & distribution; D017211:Treatment Failure; D014739:Victoria; D055815:Young Adult