Banerjee Rupa, Das Ananya, Ghoshal Uday C, Sinha Madhumita
Department of Gastroenterology, Pushpawati Singhania Research Institute for Liver, Kidney and Digestive Diseases, Raebareli Road, Lucknow 226014, India.
J Gastroenterol Hepatol. 2003 Sep;18(9):1054-60. doi: 10.1046/j.1440-1746.2003.03123.x.
Prediction of mortality from cirrhosis is important in planning optimal timing of liver transplantation and other interventions. We evaluated the role of the Artificial Neural Network (ANN), which uses non-linear statistics for pattern recognition in predicting one-year liver disease-related mortality using information available during initial clinical evaluation.
The ANN was constructed using software with data from a training set (n = 46) selected at random from a cohort of adult cirrhotics (n = 92). After training, validation was performed in the remaining patients (n = 46) whose outcome in terms of one-year mortality was unknown to the network. The performance of ANN was compared to those of a logistic regression model (LRM) and Child-Pugh's score (CPS). Death (related to cirrhosis/its complications) within one year of inclusion was the outcome variable. The ANN was also tested in an external validation sample (EVS, n = 62) from another hospital.
Patients in the EVS were younger (mean age, 41 vs 45 years), infrequently of alcoholic etiology (5% vs 49%), had less severe disease (mean CPS 6.6 vs 10.8), and had lower one-year mortality (13 vs 46%). In the internal validation sample, ANN's accuracy was 91%, sensitivity 90% and specificity 92% in prediction of one-year mortality; area under the receiver-operating characteristic (ROC) curve was 0.94. The performance of the LRM (accuracy 74%) and the CPS (accuracy 55%) was significantly worse than ANN (P < 0.05, McNemar's test). Despite differences in the characteristics of the two groups, the ANN performed fairly well in the EVS (accuracy of 90%, area under curve 0.85).
ANN can accurately predict one-year mortality in cirrhosis and is superior to CPS and LRM.
预测肝硬化患者的死亡率对于规划肝移植及其他干预措施的最佳时机至关重要。我们评估了人工神经网络（ANN）的作用，该网络使用非线性统计方法进行模式识别，利用初始临床评估时可得的信息来预测肝病相关的一年死亡率。
使用软件构建人工神经网络，数据来自从一组成年肝硬化患者（n = 92）中随机选取的训练集（n = 46）。训练后，在其余患者（n = 46）中进行验证，网络对这些患者的一年死亡率结局并不知晓。将人工神经网络的性能与逻辑回归模型（LRM）和Child-Pugh评分（CPS）的性能进行比较。纳入后一年内的死亡（与肝硬化/其并发症相关）为结局变量。人工神经网络还在来自另一家医院的外部验证样本（EVS，n = 62）中进行了测试。
EVS中的患者更年轻（平均年龄41岁对45岁），酒精性病因的发生率较低（5%对49%），疾病严重程度较轻（平均CPS 6.6对10.8），且一年死亡率较低（13%对46%）。在内部验证样本中，人工神经网络预测一年死亡率的准确率为91%，敏感性为90%，特异性为92%；受试者工作特征（ROC）曲线下面积为0.94。LRM（准确率74%）和CPS（准确率55%）的性能明显不如人工神经网络（P < 0.05，McNemar检验）。尽管两组患者特征存在差异，但人工神经网络在EVS中表现良好（准确率90%，曲线下面积0.85）。
人工神经网络能够准确预测肝硬化患者的一年死亡率，且优于CPS和LRM。
