Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, China.
J Viral Hepat. 2013 Apr;20(4):248-55. doi: 10.1111/j.1365-2893.2012.01647.x. Epub 2012 Aug 3.
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.
终末期肝病模型 (MELD) 评分最初是通过假设临床特征之间存在线性关系的传统统计学技术启动的，但临床情况中的大多数现象并非线性相关。本研究旨在使用人工神经网络 (ANN) 系统预测乙型肝炎慢加急性肝衰竭 (ACHBLF) 患者的 3 个月死亡率风险。使用来自 402 例连续 ACHBLF 患者的数据建立了 ANN 模型。该模型使用 280 例患者的数据进行训练以预测 3 个月死亡率，并使用其余 122 例患者的数据进行验证。计算 ANN 和 MELD 评分系统的接受者操作特征曲线下面积 (AUROC)。年龄 (P < 0.001)、凝血酶原活性 (P < 0.001)、血清钠 (P < 0.001)、总胆红素 (P = 0.015)、乙型肝炎 e 抗原阳性率 (P < 0.001) 和血红蛋白 (P < 0.001) 是与 ACHBLF 预后显著相关的变量，被选入 ANN 模型。ANN 在训练队列中的表现明显优于基于 MELD 的评分系统 (AUROC = 0.869 与 0.667、0.591、0.643、0.571 和 0.577；P < 0.001，分别) 和验证队列 (AUROC = 0.765 与 0.599、0.563、0.601、0.521 和 0.540；P ≤ 0.006，分别)。因此，与基于 MELD 的评分系统相比，ANN 模型在预测 ACHBLF 的 3 个月死亡率方面更准确。
