Rajanayagam, J.Frank, EibeShepherd, R. W.Lewindon, P. J.2013-07-052013-07-052013-06-272013Rajanayagam, J., Frank, E., Shepherd, R. W., & Lewindon, P. J. (2013). Artificial neural network is highly predictive of outcome in paediatric acute liver failure. Pediatric Transplantation, published online 27 June 2013.https://hdl.handle.net/10289/7752Current prognostic models in PALF are unreliable, failing to account for complex, non-linear relationships existing between multiple prognostic factors. A computational approach using ANN should provide superior modelling to PELD-MELD scores. We assessed the prognostic accuracy of PELD-MELD scores and ANN in PALF in children presenting to the QLTS, Australia. A comprehensive registry-based data set was evaluated in 54 children (32M, 22F, median age 17 month) with PALF. PELD-MELD scores calculated at (i) meeting PALF criteria and (ii) peak. ANN was evaluated using stratified 10-fold cross-validation. Outcomes were classified as good (transplant-free survival) or poor (death or LT) and predictive accuracy compared using AUROC curves. Mean PELD-MELD scores were significantly higher in non-transplanted non-survivors (i) 37 and (ii) 46 and transplant recipients (i) 32 and (ii) 43 compared to transplant-free survivors (i) 26 and (ii) 30. Threshold PELD-MELD scores ≥27 and ≥42, at meeting PALF criteria and peak, gave AUROC 0.71 and 0.86, respectively, for poor outcome. ANN showed superior prediction for poor outcome with AUROC 0.96, sensitivity 82.6%, specificity 96%, PPV 96.2% and NPV 85.7% (cut-off 0.5). ANN is superior to PELD-MELD for predicting poor outcome in PALF.enacute liver failurepediatric liver transplantationartificial neural networkpediatric end-stage liver disease scoreMachine learningJournal Article10.1111/petr.12100