This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems. Six hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models. Variables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.948, 95% CI 0.925–0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673–0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model’s predictive accuracy (AUR 0.913, 95% CI 0.887–0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697–0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05). The established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference (https://doi.org/10.1002/hep.30257).

中文翻译：

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基于人工神经网络的模型可用于预测乙型肝炎相关的慢性慢性肝衰竭患者的28天和90天死亡率

这项研究旨在开发通过人工神经网络（ANN）系统预测乙型肝炎病毒（HBV）相关的慢性慢性肝衰竭（HBV-ACLF）的28天和90天死亡率的预后模型。回顾性分析了684例连续的HBV-ACLF患者。423个案例用于训练和构建ANN模型，其余261个案例用于验证已建立的模型。通过单因素分析确定与死亡率相关的预测因素，然后将其纳入ANN模型以预测死亡率的预后。与当前各种预测模型相比，使用接收器工作特征曲线分析来评估ANN模型的预测性能。在ANN训练过程中输入具有统计学差异或具有重要临床特征的变量，并输入8个独立的危险因素，包括年龄，肝性脑病，血清钠，凝血酶原活性，γ-谷氨酰转移酶，乙型肝炎e抗原，碱性磷酸酶和总胆红素，最终用于建立ANN模型。对于训练队列中的28天死亡率，该模型的预测准确性（AUR 0.948，95％CI 0.925–0.970）显着高于终末期肝病（MELD）模型，MELD-钠（MELD-Na ），慢性肝功能衰竭-ACLF（CLIF-ACLF）和Child-Turcotte-Pugh（CTP）（所有p <0.001）。在验证队列中，ANN模型的预测准确性（AUR 0.748，95％CI：0.673–0.822）显着高于MELD的预测准确性（p = 0。0099）并显着高于MELD-Na，CTP和CLIF-ACLF（p> 0.05）。对于训练队列中90天的死亡率，该模型的预测准确性（AUR 0.913，95％CI 0.887-0.938）显着高于MELD，MELD-Na，CTP和CLIF-ACLF（均p <0.001）。在验证队列中，ANN模型的预测准确性（AUR 0.754，95％CI：0.697-0.812）显着高于MELD（p = 0.019），而显着高于MELD-Na，CTP和CLIF-ACLF（p > 0.05）。建立的ANN模型可以更准确地预测HBV-ACLF患者的短期死亡风险。主要内容已在AASLD肝病会议上作为摘要发布（https://doi.org/10.1002/hep .30257）。该模型的预测准确性（AUR 0.913，95％CI 0.887–0.938）显着高于MELD，MELD-Na，CTP和CLIF-ACLF（均p <0.001）。在验证队列中，ANN模型的预测准确性（AUR 0.754，95％CI：0.697-0.812）显着高于MELD（p = 0.019），而显着高于MELD-Na，CTP和CLIF-ACLF（p > 0.05）。建立的ANN模型可以更准确地预测HBV-ACLF患者的短期死亡风险。主要内容已在AASLD肝病会议上作为摘要发布（https://doi.org/10.1002/hep .30257）。该模型的预测准确性（AUR 0.913，95％CI 0.887-0.938）显着高于MELD，MELD-Na，CTP和CLIF-ACLF（均p <0.001）。在验证队列中，ANN模型的预测准确性（AUR 0.754，95％CI：0.697-0.812）显着高于MELD（p = 0.019），而显着高于MELD-Na，CTP和CLIF-ACLF（p > 0.05）。已建立的ANN模型可以更准确地预测HBV-ACLF患者的短期死亡风险。主要内容已在AASLD肝病会议上作为摘要发布（https://doi.org/10.1002/hep .30257）。812显着高于MELD（p = 0.019），并且显着高于MELD-Na，CTP和CLIF-ACLF（p> 0.05）。建立的ANN模型可以更准确地预测HBV-ACLF患者的短期死亡风险。主要内容已在AASLD肝病会议（https://doi.org/10.1002/hep.30257）上作为摘要张贴。812显着高于MELD（p = 0.019），并且显着高于MELD-Na，CTP和CLIF-ACLF（p> 0.05）。建立的ANN模型可以更准确地预测HBV-ACLF患者的短期死亡风险。主要内容已在AASLD肝病会议（https://doi.org/10.1002/hep.30257）上作为摘要张贴。