Artificial neural network is highly predictive of outcome in paediatric acute liver failure

Rajanayagam, J., Frank, E., Shepherd, R. W. and Lewindon, P. J. (2013) Artificial neural network is highly predictive of outcome in paediatric acute liver failure. Pediatric Transplantation, 17 6: 535-542. doi:10.1111/petr.12100

Author Rajanayagam, J.
Frank, E.
Shepherd, R. W.
Lewindon, P. J.
Title Artificial neural network is highly predictive of outcome in paediatric acute liver failure
Journal name Pediatric Transplantation   Check publisher's open access policy
ISSN 1397-3142
Publication date 2013-09
Sub-type Article (original research)
DOI 10.1111/petr.12100
Volume 17
Issue 6
Start page 535
End page 542
Total pages 8
Place of publication Malden, MA, United States
Publisher Wiley-Blackwell Publishing
Collection year 2014
Language eng
Formatted abstract
Current 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.
Keyword Acute liver failure
Pediatric liver transplantation
Artificial neural network
Pediatric end-stage liver disease score
Fulminant hepatic-failure
Paediatric liver transplantation
Paediatric end-stage liver disease score
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
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