非病毒性慢性肝病脂肪性肝纤维化的影响因素及预测模型构建

肝脏 ›› 2025, Vol. 30 ›› Issue (8): 1051-1054.

非病毒性慢性肝病脂肪性肝纤维化的影响因素及预测模型构建

贾金瑞, 张晶, 时佳, 张文思, 陈姼姼, 叶晨, 陈龙, 陆云飞, 杨宗国

201508 上海市公共卫生临床中心急诊科(贾金瑞),中医科/中西医结合科(张晶,时佳,张文思,陈姼姼,叶晨,陈龙,陆云飞,杨宗国)

收稿日期:2025-01-20 发布日期:2025-09-19
通讯作者: 杨宗国,Email: yangzongguo@shaphc.org 共同第一作者:张晶
基金资助:
上海市卫生健康委员会资助项目(2022QN044)

The risk factors and clinical model for predicting hepatic steatosis-related fibrosis in non-viral chronic liver diseases

JIA Jin-rui¹, ZHANG Jing², SHI Jia², ZHANG Wen-si², CHEN Shi-shi², YE Chen², CHEN Long², LU Yun-fei², YANG Zong-guo²

1. Department of Emergency Medicine, Shanghai Public Health Clinical Center, Shanghai 201508, China;
2. Department of Traditional Chinese Medicine, Shanghai Public Health Clinical Center, Shanghai 201508, China

Received:2025-01-20 Published:2025-09-19
Contact: YANG Zong-guo,Email: yangzongguo@shaphc.org

摘要/Abstract

摘要： 目的评价非病毒性慢性肝病发生脂肪性肝纤维化的影响因素,构建其临床预测模型。方法回顾性分析非病毒性慢性肝病患者的临床资料。通过logistic回归模型分析影响脂肪性肝纤维化的因素,以此构建预测模型,通过OptimalCutpoints函数评价模型预测价值。结果纳入153例患者,19例(12.4%)发生脂肪性肝纤维化。单因素及多因素logistic回归分析显示,肝瞬时弹性值(OR=1.09,95%CI:1.03~1.15,P=0.002)、甘油三酯(OR=6.17,95%CI :1.29~29.48,P=0.023)为非病毒性慢性肝病发生脂肪性肝纤维化的危险因素。构建模型Model = 1.09×肝脏弹性值+6.17×甘油三酯(异常,1;正常,0)。通过OptimalCutpoints包分析,Model模型的最佳cut-off值为10.79,灵敏度为0.95,特异度为0.54,阳性预测值0.23,阴性预测值为0.986,AUC=0.77。而肝脏弹性值和甘油三酯的AUC分别为0.71和0.66。本模型预测价值最高。结论肝脏弹性值联合甘油三酯使用可预测非病毒性慢性肝病发生脂肪性肝纤维化的风险。

关键词: 肝脂肪变, 肝纤维化, 模型, 甘油三酯, 肝瞬时弹性超声

Abstract: Objective This study aims to evaluate the risk factors for hepatic steatosis-related fibrosis in non-viral chronic liver diseases, and further construct its clinical prediction model. Methods Clinical records and pathological data from non-viral chronic liver diseases were retrospectively collected. Logistic methods were used to identify the risk factors for hepatic steatosis-related fibrosis and construct a predictive model. The predictive values of this model were further calculated using OptimalCutpoints package in the R software. Results A Total of 153 cases were included in this study within which 19 (12.4%) patients developed hepatic steatosis-related fibrosis. Univariate and multivariate logistic regression analysis showed that liver stiffness measurement (LSM) and triglycerides were risk factors for hepatic steatosis-related fibrosis in non-viral chronic liver diseases (OR=1.09, 95% CI=1.03~1.15, P=0.002 and OR=6.17, 95% CI=1.29~29.48, P=0.023; respectively). A predictive Model =1.09 × LSM + 6.17 × triglycerides (abnormal, 1; normal, 0) was constructed. The optimal cut-off value of the Model is 10.79, with a sensitivity of 0.95, specificity of 0.54, positive predictive value of 0.23, and negative predictive value of 0.986. Compared with the LSM (AUC=0.71), this model has a higher predictive value (AUC=0.77) and shows a statistically significant trend (P=0.071). Conclusion LSM and serum triglycerides can be used to predict the occurrence of hepatic steatosis-related fibrosis in patients with non-viral chronic liver disease.

Key words: Hepatis steatosis, Liver fibrosis, Model, Triglyceride, Liver stiffness measurement

贾金瑞, 张晶, 时佳, 张文思, 陈姼姼, 叶晨, 陈龙, 陆云飞, 杨宗国. 非病毒性慢性肝病脂肪性肝纤维化的影响因素及预测模型构建[J]. 肝脏, 2025, 30(8): 1051-1054.

JIA Jin-rui, ZHANG Jing, SHI Jia, ZHANG Wen-si, CHEN Shi-shi, YE Chen, CHEN Long, LU Yun-fei, YANG Zong-guo. The risk factors and clinical model for predicting hepatic steatosis-related fibrosis in non-viral chronic liver diseases[J]. Chinese Hepatolgy, 2025, 30(8): 1051-1054.

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