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Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation
Author(s) -
Ayllón María Dolores,
Ciria Rubén,
CruzRamírez Manuel,
PérezOrtiz María,
Gómez Irene,
Valente Roberto,
O'Grady John,
de la Mata Manuel,
HervásMartínez César,
Heaton Nigel D.,
Briceño Javier
Publication year - 2018
Publication title -
liver transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.814
H-Index - 150
eISSN - 1527-6473
pISSN - 1527-6465
DOI - 10.1002/lt.24870
Subject(s) - medicine , liver transplantation , matching (statistics) , artificial neural network , transplantation , living donor liver transplantation , liver disease , model for end stage liver disease , cohort , receiver operating characteristic , stage (stratigraphy) , artificial intelligence , machine learning , surgery , computer science , pathology , paleontology , biology
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.