Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Journal: Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
PMID:

Abstract

In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to test the ANN-based methodology in a different European health care system in order to validate it. An ANN model was designed using a cohort of patients from King's College Hospital (KCH; n = 822). The ANN was trained and tested using KCH pairs for both 3- and 12-month survival models. End points were probability of graft survival (correct classification rate [CCR]) and nonsurvival (minimum sensitivity [MS]). The final model is a rule-based system for facilitating the decision about the most appropriate D-R matching. Models designed for KCH had excellent prediction capabilities for both 3 months (CCR-area under the curve [AUC] = 0.94; MS-AUC = 0.94) and 12 months (CCR-AUC = 0.78; MS-AUC = 0.82), almost 15% higher than the best obtained by other known scores such as Model for End-Stage Liver Disease and balance of risk. Moreover, these results improve the previously reported ones in the multicentric MADR-E database. In conclusion, the use of ANN for D-R matching in LT in other health care systems achieved excellent prediction capabilities supporting the validation of these tools. It should be considered as the most advanced, objective, and useful tool to date for the management of waiting lists. Liver Transplantation 24 192-203 2018 AASLD.

Authors

  • María Dolores Ayllón
    Unit of Hepatobiliary Surgery and Liver Transplantation, Córdoba, Spain.
  • Rubén Ciria
    Liver Transplantation Unit, Reina Sofía Hospital, Av. Menéndez Pidal, 14004 Córdoba, Spain.
  • Manuel Cruz-Ramírez
    Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain.
  • María Pérez-Ortiz
    Department of Quantitative Methods, Universidad Loyola Andalucía, Escritor Castilla Aguayo 4, 14004 Córdoba, Spain.
  • Irene Gómez
    Unit of Hepatobiliary Surgery and Liver Transplantation, Córdoba, Spain.
  • Roberto Valente
    Unit of Digestive Surgery, Hepato-Pancreato-Biliary Surgery, and Liver Transplantation, Henri Mondor Hospital, AP-HP, Créteil, France., Hepato-Pancreato-Biliary-Liver Transplant Surgery Unit, The Royal Free Hospital, London, UK Unit of Digestive Surgery, Hepato-Pancreato-Biliary Surgery, and Liver Transplantation, Henri Mondor Hospital, AP-HP, Créteil, France, Inserm, Unité 955-IMRB, Créteil, France.
  • John O'Grady
    Institute of Liver Studies, King's Health Partners at King's College Hospital, London, United Kingdom.
  • Manuel de la Mata
    Liver Research Unit, Liver Transplantation Unit, University Hospital Reina Sofia, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Instituto Maimónides de Investigación Biomédica de Córdoba, Córdoba, Spain.
  • César Hervás-Martínez
    Department of Computer Science and Numerical Analysis, University of Córdoba, Campus Universitario de Rabanales, "Albert Einstein Building", Third Floor, 14071 Córdoba, Spain.
  • Nigel D Heaton
    Institute of Liver Studies, King's Health Partners at King's College Hospital, London, United Kingdom.
  • Javier Briceño
    Liver Transplantation Unit, Reina Sofía Hospital, Av. Menéndez Pidal, 14004 Córdoba, Spain.