Construction of a short-term prognostic model for patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure based on machine learning

Ren Jing; Bai Chengzhi; Su Shaohui; Tang Shanhong

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Construction of a short-term prognostic model for patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure based on machine learning

更新时间：2026-07-01

- Construction of a short-term prognostic model for patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure based on machine learning
- Journal of Chongqing Medical University Pages: 1-8(2026)
- 作者机构：
  
  1.川北医学院临床医学院，南充 637000
  2.中国人民解放军西部战区总医院消化内科，成都 610083
- 作者简介：
- 基金信息：
- DOI：
  CLC： R575.3
- Received：22 March 2026，
  
  Revised：2026-06-05，
  
  Accepted：08 June 2026，
  
  Published：01 July 2026
- 稿件说明：
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Ren Jing,Bai Chengzhi,Su Shaohui,et al.Construction of a short-term prognostic model for patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure based on machine learning[J].Journal of Chongqing Medical University,
DOI：

Ren Jing,Bai Chengzhi,Su Shaohui,et al.Construction of a short-term prognostic model for patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure based on machine learning[J].Journal of Chongqing Medical University, DOI：10.13406/j.cnki.cyxb..

摘要

目的

基于机器学习构建失代偿期乙型肝炎肝硬化并发慢加急性肝衰竭（acute-on-chronic liver failure，ACLF）患者的90 d预后模型，并评估其预测效能。

方法

回顾性收集2015年1月至2023年1月于西部战区总医院收治的218例失代偿期乙型肝炎肝硬化并发ACLF患者的临床资料，将患者以7∶3的比例随机分为训练集（153例）和验证集（65例），训练集用于构建模型，验证集用于验证模型；通过最小绝对收缩与选择算子（least absolute shrinkage and selection operator，LASSO）回归分析方法筛选与其预后相关的关键变量，采用Logistic回归、梯度提升机（Gradient Boosting Machine，GBM）、随机森林（Random Forest）、K近邻算法（K-nearest neighbor，KNN）、轻量级梯度提升机（Light Gradient Boosting Machine，LightGBM）等10种机器学习算法构建失代偿期乙型肝炎肝硬化并发ACLF患者90 d短期预测模型，绘制受试者工作特征曲线（receiver operating characteristic curve，ROC），比较ROC曲线下面积（area under the curve，AUC）、准确度、灵敏度、特异度和F1分数等指标评估不同模型的预测效能。通过沙普利可加性解释（Shapley additive explanations，SHAP）分析各关键变量对最优模型预测结果的贡献度并排序。

结果

通过LASSO回归筛选出6个变量，基于此构建10种机器学习模型，其中LightGBM模型预测效能最佳，AUC为0.813，准确度为0.769，SHAP图显示重要性排序分别为总胆红素（total bilirubin，TBIL）、国际标准化比值（international normalized ratio，INR）、肝性脑病（hepatic encephalopathy，HE）、白细胞（white blood cell count，WBC）、年龄（age）和低密度脂蛋白胆固醇（low-density lipoprotein cholesterol，LDL-C）。

结论

成功构建出预测失代偿期乙型肝炎肝硬化并发ACLF患者90 d预后的模型，其中LightGBM模型预测效能最佳，有助于识别高风险患者，为临床决策及干预提供依据。

Abstract

Objective

To construct a predictive model for the 90-day prognosis of patients with decompensated hepatitis B cirrhosis complicated by acute-on-chronic liver failure（ACLF），and to assess its predictive efficacy.

Methods

A retrospective analysis was performed for the clinical data of 218 patients with decompensated hepatitis B cirrhosis complicated by ACLF who were admitted to The General Hospital of Western Theater Command from January 2015 to January 2023，and these patients were randomly divided into a training set with 153 patients and a validation set with 65 patients at a ratio of 7∶3，with the training set used for model construction and the validation set for model validation. The least absolute shrinkage and selection operator（LASSO） regression analysis was used to identify the key variables associated with prognosis，and 10 machine learning algorithms were used to construct a predictive model for the 90-day short-term prognosis of patients with decompensated hepatitis B cirrhosis complicated by ACLF，including logistic regression model，gradient boosting machine（GBM），random forest，k-nearest neighbor（KNN），and light gradient boosting machine （LightGBM）. The receiver operating characteristic（ROC） curve was plotted，and the area under the ROC curve（AUC），accuracy，sensitivity，specificity，and F1-score of different models were compared to assess their predictive efficacy. Shapley Additive exPlanations（SHAP） was used to analyze and rank the contribution of key variables to the prediction results of the optimal model.

Results

Six variables were identified by LASSO regression，and 10 machine learning models were constructed based on these variables，among which the LightGBM model showed the optimal predictive performance，with an AUC of 0.813 and an accuracy of 0.769. The SHAP plots showed that total bilirubin had the greatest importance，followed by international normalized ratio，hepatic encephalopathy，white blood cell count，age，and low-density lipoprotein cholesterol.

Conclusion

This study successfully constructed the models for predicting the 90-day prognosis of patients with decompensated hepatitis B cirrhosis complicated by ACLF，among which the LightGBM model has the best predictive performance and can help to identify high-risk patients and provide a basis for clinical decision-making and intervention.

关键词

Keywords

references

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