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dc.contributor.authorAyllón, María Dolores
dc.contributor.authorCiria, Rubén
dc.contributor.authorCruz-Ramírez, Manuel
dc.contributor.authorPérez-Ortiz, María
dc.contributor.authorGómez, Irene
dc.contributor.authorValente, Roberto
dc.contributor.authorO'Grady, John
dc.contributor.authorde la Mata, Manuel
dc.contributor.authorHervás-Martínez, César
dc.contributor.authorHeaton, Nigel D
dc.contributor.authorBriceño, Javier
dc.description.abstractIn 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.
dc.subject.meshArea Under Curve
dc.subject.meshComputer Simulation
dc.subject.meshDecision Support Techniques
dc.subject.meshDonor Selection
dc.subject.meshGraft Survival
dc.subject.meshLiver Diseases
dc.subject.meshLiver Transplantation
dc.subject.meshMiddle Aged
dc.subject.meshNeural Networks, Computer
dc.subject.meshROC Curve
dc.subject.meshReproducibility of Results
dc.subject.meshRisk Assessment
dc.subject.meshRisk Factors
dc.subject.meshTime Factors
dc.subject.meshTissue Donors
dc.subject.meshTreatment Outcome
dc.subject.meshWaiting Lists
dc.titleValidation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.
dc.typeresearch article
dc.rights.accessRightsopen access
dc.journal.titleLiver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
dc.journal.titleabbreviationLiver Transpl
dc.organizationInstituto Maimónides de Investigación Biomédica de Córdoba-IMIBIC
dc.organizationHospital Universitario Reina Sofía
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.pubmedtypeValidation Study
Appears in Collections:Producción 2020

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