Premium
A model to predict 3‐month mortality risk of acute‐on‐chronic hepatitis B liver failure using artificial neural network
Author(s) -
Zheng M.H.,
Shi K.Q.,
Lin X.F.,
Xiao D.D.,
Chen L.L.,
Liu W.Y.,
Fan Y.C.,
Chen Y.P.
Publication year - 2013
Publication title -
journal of viral hepatitis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.329
H-Index - 100
eISSN - 1365-2893
pISSN - 1352-0504
DOI - 10.1111/j.1365-2893.2012.01647.x
Subject(s) - liver failure , chronic hepatitis , artificial neural network , medicine , artificial liver , intensive care medicine , artificial intelligence , computer science , immunology , virus
Summary Model for end‐stage liver disease ( MELD ) scoring was initiated using traditional statistical technique by assuming a linear relationship between clinical features, but most phenomena in a clinical situation are not linearly related. The aim of this study was to predict 3‐month mortality risk of acute‐on‐chronic hepatitis B liver failure ( ACHBLF ) on an individual patient level using an artificial neural network ( ANN ) system. The ANN model was built using data from 402 consecutive patients with ACHBLF . It was trained to predict 3‐month mortality by the data of 280 patients and validated by the remaining 122 patients. The area under the curve of receiver operating characteristic ( AUROC ) was calculated for ANN and MELD ‐based scoring systems. The following variables age ( P < 0.001), prothrombin activity ( P < 0.001), serum sodium ( P < 0.001), total bilirubin ( P = 0.015), hepatitis B e antigen positivity rate ( P < 0.001) and haemoglobin ( P < 0.001) were significantly related to the prognosis of ACHBLF and were selected to build the ANN . The ANN performed significantly better than MELD ‐based scoring systems both in the training cohort ( AUROC = 0.869 vs 0.667, 0.591, 0.643, 0.571 and 0.577; P < 0.001, respectively) and in the validation cohort ( AUROC = 0.765 vs 0.599, 0.563, 0.601, 0.521 and 0.540; P ≤ 0.006, respectively). Thus, the ANN model was shown to be more accurate in predicting 3‐month mortality of ACHBLF than MELD ‐based scoring systems.