title: Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.
abstract: OBJECTIVE
  There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival.
  
  
  METHODS
  64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores.
  
  
  RESULTS
  Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48).
  
  
  CONCLUSIONS
  ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models.
authors.affiliation: Liver Transplantation Unit, CIBERehd, IMIBIC, Hospital Reina Sofía, Córdoba, Spain. Electronic address: javibriceno@hotmail.com.
authors.forename: Javier
authors.initials: J
authors.lastname: Briceño
authors.affiliation: Department of Computer Science and Numerical Analysis, University of Córdoba, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Cruz-Ramírez
authors.affiliation: Liver Transplantation Unit, CIBERehd, Hospital La Fe, Valencia, Spain.
authors.forename: Martín
authors.initials: M
authors.lastname: Prieto
authors.affiliation: Liver Transplantation Unit, Hospital Clínic, Barcelona, Spain.
authors.forename: Miguel
authors.initials: M
authors.lastname: Navasa
authors.affiliation: Liver Transplantation Unit, Hospital de Cruces, Bilbao, Spain.
authors.forename: Jorge
authors.initials: J
authors.lastname: Ortiz de Urbina
authors.affiliation: Liver Transplantation Unit, CIBERehd, IMIBIC, Hospital Reina Sofía, Córdoba, Spain.
authors.forename: Rafael
authors.initials: R
authors.lastname: Orti
authors.affiliation: Liver Transplantation Unit, Hospital Virgen del Rocío, Sevilla, Spain.
authors.forename: Miguel-Ángel
authors.initials: MÁ
authors.lastname: Gómez-Bravo
authors.affiliation: Liver Transplantation Unit, Hospital Juan Canalejo, A Coruña, Spain.
authors.forename: Alejandra
authors.initials: A
authors.lastname: Otero
authors.affiliation: Liver Transplantation Unit, Hospital Clínico Universitario, Santiago de Compostela, Spain.
authors.forename: Evaristo
authors.initials: E
authors.lastname: Varo
authors.affiliation: Liver Transplantation Unit, Hospital Clínico Universitario, Santiago de Compostela, Spain.
authors.forename: Santiago
authors.initials: S
authors.lastname: Tomé
authors.affiliation: Liver Transplantation Unit, Hospital Gregorio Marañón, Madrid, Spain.
authors.forename: Gerardo
authors.initials: G
authors.lastname: Clemente
authors.affiliation: Liver Transplantation Unit, Hospital Gregorio Marañón, Madrid, Spain.
authors.forename: Rafael
authors.initials: R
authors.lastname: Bañares
authors.affiliation: Liver Transplantation Unit, Hospital Ramón y Cajal, Madrid, Spain.
authors.forename: Rafael
authors.initials: R
authors.lastname: Bárcena
authors.affiliation: Liver Transplantation Unit, Hospital Puerta de Hierro, Madrid, Spain.
authors.forename: Valentín
authors.initials: V
authors.lastname: Cuervas-Mons
authors.affiliation: Liver Transplantation Unit, Hospital Infanta Cristina, Badajoz, Spain.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Solórzano
authors.affiliation: Liver Transplantation Unit, CIBERehd, Hospital La Fe, Valencia, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Vinaixa
authors.affiliation: Liver Transplantation Unit, CIBERehd, Hospital La Fe, Valencia, Spain.
authors.forename: Angel
authors.initials: A
authors.lastname: Rubín
authors.affiliation: Liver Transplantation Unit, Hospital Clínic, Barcelona, Spain.
authors.forename: Jordi
authors.initials: J
authors.lastname: Colmenero
authors.affiliation: Liver Transplantation Unit, Hospital de Cruces, Bilbao, Spain.
authors.forename: Andrés
authors.initials: A
authors.lastname: Valdivieso
authors.affiliation: Liver Transplantation Unit, CIBERehd, IMIBIC, Hospital Reina Sofía, Córdoba, Spain.
authors.forename: Rubén
authors.initials: R
authors.lastname: Ciria
authors.affiliation: Department of Computer Science and Numerical Analysis, University of Córdoba, Spain.
authors.forename: César
authors.initials: C
authors.lastname: Hervás-Martínez
authors.affiliation: Liver Transplantation Unit, CIBERehd, IMIBIC, Hospital Reina Sofía, Córdoba, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: de la Mata
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D005260:Female; D006085:Graft Survival; D006801:Humans; D016031:Liver Transplantation / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D011379:Prognosis; D013030:Spain; D014019:Tissue Donors; D066027:Transplant Recipients; D055815:Young Adult
keywords: Allocation; Artificial intelligence; Optimization; Prediction; Survival