title: Self-organizing maps can determine outcome and match recipients and donors at orthotopic liver transplantation.
abstract: BACKGROUND
  There is a relative lack of donor organs for liver transplantation. Ideally, to maximize the utility of those livers that are offered, donor and recipient characteristics should be matched to ensure the best possible posttransplant survival of the recipient.
  
  
  METHODS
  With prospectively collected data on 827 patients receiving a primary liver graft for chronic liver disease, we used a self-organizing map (SOM) (one form of a neural network) to predict outcome after transplantation using both donor and recipient factors. The SOM was then validated using a data set of 2622 patients undergoing transplantation in the United Kingdom at other centers.
  
  
  RESULTS
  SOM analysis using 72 inputs and two survival intervals (3 and 12 months) yielded three neurons with either higher or lower probabilities of survival. The model was validated using the independent data set. With 20 patients on the waiting list and 10 sequential donor livers, it was possible to demonstrate that the model could be used to identify which potential recipients were likely to benefit most from each liver offered.
  
  
  CONCLUSIONS
  With this approach to matching donor livers and recipients, it is possible to inform transplant clinicians about the optimum use of donor livers and thereby effectively make the best use of a scarce resource.
authors.affiliation: Liver Unit, Third Floor, Nuffield House, The Queen Elizabeth Hospital, Birmingham, UK. Geoffrey.Haydon@uhb.nhs.uk
authors.forename: Geoffrey H
authors.initials: GH
authors.lastname: Haydon
authors.forename: Yrjo
authors.initials: Y
authors.lastname: Hiltunen
authors.forename: Michael R
authors.initials: MR
authors.lastname: Lucey
authors.forename: David
authors.initials: D
authors.lastname: Collett
authors.forename: Bridget
authors.initials: B
authors.lastname: Gunson
authors.forename: Nick
authors.initials: N
authors.lastname: Murphy
authors.forename: Peter G
authors.initials: PG
authors.lastname: Nightingale
authors.forename: James
authors.initials: J
authors.lastname: Neuberger
mesh_terms: D015897:Comorbidity; D005006:Ethnicity; D005260:Female; D006498:Hepatectomy / Q000379:methods*; D006650:Histocompatibility Testing; D006801:Humans; D016031:Liver Transplantation / Q000401:mortality / Q000502:physiology / Q000706:statistics & numerical data*; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011336:Probability; D016019:Survival Analysis; D014019:Tissue Donors / Q000706:statistics & numerical data*; D020858:Tissue and Organ Harvesting / Q000379:methods*; D016896:Treatment Outcome