基于LASSO回归建立慢性乙型肝炎肝硬化患者继发肝性脑病的预测模型

肝脏 ›› 2026, Vol. 31 ›› Issue (2): 172-176.

基于LASSO回归建立慢性乙型肝炎肝硬化患者继发肝性脑病的预测模型

叶小欣, 李卫, 郜玉峰, 肖安岭, 刘南南

236015 阜阳安徽省阜阳市第二人民医院肝脏病科(叶小欣,李卫,肖安岭,刘南南);230000 合肥安徽医科大学第一附属医院感染病科(郜玉峰)

收稿日期:2025-01-08 出版日期:2026-02-28 发布日期:2026-04-17
通讯作者: 李卫,Email:13705580102@163.com
基金资助:
安徽省医学临床研究转化专项立项项目(202304295107020040)

The establishment of a predictive model for hepatic encephalopathy secondary to chronic hepatitis B-related cirrhosis based on LASSO regression

YE Xiao-xin¹, LI Wei¹, GAO Yu-feng², XIAO An-ling¹, LIU Nan-nan¹

1. Department of Hepatology, Second People's Hospital of Fuyang, Fuyang 236015, China;
2. Department of Infectious Diseases, First Affiliated Hospital of Anhui Medical University, Hefei 230000, China

Received:2025-01-08 Online:2026-02-28 Published:2026-04-17
Contact: LI Wei,Email:13705580102@163.com

摘要/Abstract

摘要： 目的基于LASSO回归建立慢性乙型肝炎肝硬化患者继发肝性脑病的预测模型。方法选择2022年1月至2024年1月在阜阳市第二人民医院接受治疗的慢性乙型肝炎肝硬化患者189例,其中继发肝性脑病的患者61例。收集患者的基线资料,比较两组基本资料和生化指标,通过LASSO-logistic回归模型筛选乙型肝炎肝硬化患者继发肝性脑病的风险因素,评价模型拟合效果,采用校准曲线验证。结果继发肝性脑病患者的年龄、ALT、AST、TBil、ALP、血氨和MELD评分分别为(56.2±7.2)岁、(32.15±4.51)U/L、(46.72±5.48)U/L、(40.01±5.53)μmol/L、(95.67±10.28)U/L、(51.03±7.34)μmol/L、(10.13±2.54)分,均高于无肝性脑病患者的(53.0±4.5)岁、(28.83±4.10)U/L、(40.88±5.11)U/L、(28.78±5.11)μmol/L、(89.14±9.77)U/L、(30.28±5.93)μmol/L、(7.23±8.205)分,而Hb和Alb水平更低,差异均有统计学意义(P<0.05)。根据LASSO-logistic 回归分析结果,高龄、高血氨、高终末期肝病模型(MELD)评分、低Alb均是患者发生肝性脑病的独立风险因素,LASSO-logistic 回归模型的 AIC 以及 BIC 分别为20.221和39.672,拟合效果较好。结论 LASSO-logistic回归模型拟合效果较好,有助于提高慢性乙型肝炎肝硬化患者继发肝性脑病的预测准确度。

关键词: 慢性乙型肝炎肝硬化, 肝性脑病, LASSO回归, 风险因素分析

Abstract: Objective This study aims to establish a predictive model for hepatic encephalopathy secondary to chronic hepatitis B-related cirrhosis using LASSO regression analysis. Methods The study population consisted of 189 patients with chronic hepatitis B-related cirrhosis admitted from January 2022 to January 2024. Patients with hepatic encephalopathy were designated as the observation group, while the others were served as the control group. The baseline data and biochemical indices of all patients were collected and compared between the two groups. LASSO-logistic regression analysis was employed to identify the predictive factors for hepatic encephalopathy and construct a predictive mode. The model′s fit was evaluated using the akaike information criterion (AIC) and bayesian information criterion (BIC), comparing the traditional logistic and LASSO-logistic regression models, and validated by calibration curves. Results Among the 189 patients with hepatitis B-related cirrhosis, 32.28% (61/189) developed hepatic encephalopathy. The baseline data of patients in the observation group were as the following: age (56.2±7.2) years, alanine aminotransferase (ALT) (32.15±4.51) U/L, aspartate aminotransferase (AST) (46.72±5.48) U/L, total bilirubin (TBil) (40.01±5.53) μmol/L, alkaline phosphatase (ALP) (95.67±10.28) U/L, blood ammonia (51.03±7.34) μmol/L, and model for end-stage liver disease (MELD) score (10.13±2.54), all of which were higher than those of (53.0±4.5) years, (28.83±4.10) U/L, (40.88±5.11) U/L, (28.78±5.11) μmol/L, (89.14±9.77) U/L, (30.28±5.93) μmol/L, and (7.23±8.205), respectively, in patients of the control group. Additionally, the levels of hemoglobin (Hb) and albumin (Alb) were lower than those of the observation group, with statistically significant differences (P<0.05). According to LASSO-logistic regression analysis, advanced age, high blood ammonia, high MELD score, and low albumin (Alb) were identified as independent risk factors for the development of hepatic encephalopathy. The AIC and BIC values for the LASSO-logistic regression model were 20.221 and 39.672, respectively, indicating a good fit of the model. Conclusion The LASSO-logistic regression model, based on the selected variables, shows good fit and predictive accuracy for assessing hepatic encephalopathy in patients with chronic hepatitis B-related cirrhosis.

Key words: Chronic hepatitis B-related cirrhosis, Hepatic encephalopathy, LASSO regression analysis, Risk factor analysis

叶小欣, 李卫, 郜玉峰, 肖安岭, 刘南南. 基于LASSO回归建立慢性乙型肝炎肝硬化患者继发肝性脑病的预测模型[J]. 肝脏, 2026, 31(2): 172-176.

YE Xiao-xin, LI Wei, GAO Yu-feng, XIAO An-ling, LIU Nan-nan. The establishment of a predictive model for hepatic encephalopathy secondary to chronic hepatitis B-related cirrhosis based on LASSO regression[J]. Chinese Hepatolgy, 2026, 31(2): 172-176.

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