慢性乙型肝炎肝硬化患者继发肝性脑病风险预测模型的构建与验证

肝脏 ›› 2023, Vol. 28 ›› Issue (8): 916-920.

慢性乙型肝炎肝硬化患者继发肝性脑病风险预测模型的构建与验证

张家齐, 王再超, 杨家耀, 王晓梦, 赵玉

430065 湖北武汉市中西医结合医院(张家齐,杨家耀);湖北中医药大学(王再超,王晓梦,赵玉)

收稿日期:2022-11-06 出版日期:2023-08-31 发布日期:2023-09-21
通讯作者: 王再超,Email:305898764@qq.com

Construction and validation of the risk prediction model for secondary hepatic encephalopathy in patients with chronic hepatitis B and cirrhosis

ZHANG Jia-qi¹, WANG Zai-chao², YANG Jia-yao¹, WANG Xiao-meng², ZHAO Yu²

1. Department of Gastroenterology, Wuhan Hospital of Integrated Traditional Chinese and Western Medicine, Hubei 430065, China;
2. Hubei University of Chinese Medicine,Wuhan 430065, China

Received:2022-11-06 Online:2023-08-31 Published:2023-09-21
Contact: WANG Zai-chao,Email:305898764@qq.com

摘要/Abstract

摘要： 目的探讨慢性乙型肝炎(CHB)肝硬化患者继发肝性脑病(HE)的危险因素,并构建与验证风险预测模型。方法回顾性分析2021年1月至2022年6月收治的202例CHB肝硬化患者的临床资料,其中HE组59例和非HE组143例。采用logistic回归分析危险因素,采用ROC曲线验证预测模型的预测效能。结果 logistic回归分析结果显示,接受经颈内静脉肝内门体分流术(OR=3.043)、合并上消化道出血(OR=4.007)、合并电解质酸碱平衡紊乱(OR=2.401)、合并肝肾综合征(OR=4.540)、合并便秘(OR=2.838)是CHB肝硬化患者继发HE的危险因素(P<0.05)。风险预测模型:P=ex/(1+ex),X=-2.375+1.113×经颈内静脉肝内门体分流术+1.388×上消化道出血+0.876×电解质酸碱平衡紊乱+1.513×肝肾综合征+1.043×便秘。ROC曲线下面积为0.841(95%CI=0.772,P<0.001),灵敏度为80.36%,特异度为80.00%,约登指数为0.604。结论 CHB肝硬化患者HE预测模型能够较好地预测HE的发生风险。

关键词: 慢性乙型肝炎, 肝硬化, 肝性脑病, 预测模型

Abstract: Objective To investigate the risk factors of secondary hepatic encephalopathy (HE) in patients with chronic hepatitis B (CHB) and cirrhosis, construct a risk prediction model, and verify it.Methods A total of 202 patients with CHB and cirrhosis admitted to the hospital from January 2021 to June 2022 were retrospectively analyzed. According to the presence or absence of secondary HE, the patients were divided into HE group (59 cases) and non-HE group (143 cases). Clinical data of the patients were collected. The risk factors were screened by logistic regression analysis, and a prediction model was constructed. The ROC curve was used to verify the predictive performance of this model.Results Logistic regression analysis results showed that transjugular intrahepatic portosystemic stent-shunts (TIPS, OR=3.043), upper gastrointestinal bleeding (OR=4.007), electrolyte acid-base balance disorder (OR=2.401), hepatorenal syndrome (OR=4.540) and constipation (OR=2.838) were risk factors of secondary HE (P<0.05). The risk prediction model was as follow: P=ex/(1+ex), X=-2.375+1.113×TIPS+1.388×upper gastrointestinal bleeding+0.876×electrolyte acid-base balance disorder+1.513× hepatorenal syndrome+1.043×constipation. The area under the ROC curve (AUC), sensitivity, specificity and Youden index were 0.841 (95%CI=0.772, P<0.001), 80.36%, 80.00% and 0.604, respectively.Conclusion The prediction model can help to better predict the occurrence of HE in patients with CHB and cirrhosis. Clinically, the model can be used to evaluate patients with CHB and cirrhosis, thereby assisting in targeted measures to prevent the occurrence of HE.

Key words: Chronic hepatitis B, Cirrhosis, Hepatic encephalopathy, Prediction model

张家齐, 王再超, 杨家耀, 王晓梦, 赵玉. 慢性乙型肝炎肝硬化患者继发肝性脑病风险预测模型的构建与验证[J]. 肝脏, 2023, 28(8): 916-920.

ZHANG Jia-qi, WANG Zai-chao, YANG Jia-yao, WANG Xiao-meng, ZHAO Yu. Construction and validation of the risk prediction model for secondary hepatic encephalopathy in patients with chronic hepatitis B and cirrhosis[J]. Chinese Hepatolgy, 2023, 28(8): 916-920.

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