1.武汉大学中南医院消化内科/肠病湖北省重点实验室 湖北 武汉 430071
2.鄂州市中心医院消化内科 湖北 鄂州 436000
宋莹,女,2000—,医学硕士生,主要从事肝病及消化内镜研究,E⁃mail: 744662539@qq.com
杨梅,女,1983—,副主任医师,主要从事肠道疾病研究,E⁃mail: 928783138@qq.com
刘静,E⁃mail: liujing_GI@whu.edu.cn。
收稿:2025-10-19,
纸质出版:2026-10-25
移动端阅览
宋莹,杨梅,蔡沐等.肠道屏障功能在肝硬化肝性脑病诊断中的价值:一项回顾性研究[J].武汉大学学报(医学版),2026,47(10):1325-1329.
SONG Ying,YANG Mei,CAI Mu,et al.Value of intestinal barrier function in the diagnosis of cirrhosis hepatic encephalopathy: A retrospective study[J].Medical Journal of Wuhan University,2026,47(10):1325-1329.
宋莹,杨梅,蔡沐等.肠道屏障功能在肝硬化肝性脑病诊断中的价值:一项回顾性研究[J].武汉大学学报(医学版),2026,47(10):1325-1329. DOI: 10.14188/j.1671-8852.2025.1212.
SONG Ying,YANG Mei,CAI Mu,et al.Value of intestinal barrier function in the diagnosis of cirrhosis hepatic encephalopathy: A retrospective study[J].Medical Journal of Wuhan University,2026,47(10):1325-1329. DOI: 10.14188/j.1671-8852.2025.1212.
目的
2
探讨二胺氧化酶、
D
⁃乳酸及细菌内毒素等肠道屏障指标对肝性脑病的诊断价值。
方法
2
该研究为单中心回顾性观察,研究对象为2023年1月至2024年12月期间接受肠道屏障检测的肝硬化合并肝性脑病患者。研究从二胺氧化酶、
D
⁃乳酸、细菌内毒素、血氨、Child⁃Pugh分级等指标中筛选肝性脑病的危险因素,并据此构建肝性脑病的诊断模型。
结果
2
共纳入230例患者,其中男性为主(61.7%),平均年龄60.17岁。肝硬化主要病因为乙型肝炎(43.9%)。按8∶2比例将患者分为训练集与测试集,训练集
AUC
达0.94,测试集
AUC
为0.883,表明该模型具有良好泛化能力。
结论
2
细菌内毒素是肝性脑病的重要危险因素。基于细菌内毒素等指标构建的诊断模型对肝性脑病可能具有良好的诊断价值,但尚需外部数据进一步验证。
Objective
2
To explore the diagnostic value of intestinal barrier indicators
including diamine oxidase
D
⁃lactate
and bacterial endotoxin
in hepatic encephalopathy.
Methods
2
A retrospective
monocentric study was conducted in the patients with cirrhosis and hepatic encephalopathy undergoing intestinal barrier testing between January 2023 and December 2024. The risk factors of hepatic encephalopathy were screened from diamine oxidase
D
⁃lactate
bacterial endotoxin
blood ammonia
Child⁃Pugh grade
etc.
and the diagnostic model of hepatic encephalopathy was constructed accordingly.
Results
2
In total
230 patients were included. The patients were predominantly male (61.7%)
with an average age of 60.17 years. The main etiology of cirrhosis was hepatitis B (43.9%). The patients were divided into the training set and the test set according to the ratio of 8∶2
and the
AUC
of the training set was 0.94 and the
AUC
of the test set was 0.883
indicating that the model had good generalization ability.
Conclusion
2
Bacterial endotoxin constitutes a significant risk factor for hepatic encephalopathy. The diagnostic model constructed based on the indices including bacterial endotoxin may have good diagnostic value for hepatic encephalopathy. Further external data are needed for verification.
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