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

Dolores Ayllón, M; Ciria, R; Cruz-Ramirez, M; Perez-Ortiz, M; Gómez, I; Valente, R; O'Grady, J; ... Briceño, J; + view all (2018) Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation. Liver Transplantation , 24 (2) pp. 192-203. 10.1002/lt.24870. Green open access

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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.

Type: Article
Title: Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/lt.24870
Publisher version: https://doi.org/10.1002/lt.24870
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10074842
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