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慢性乙型肝炎病毒感染合并非酒精性脂肪性肝病患者中一种新型的HBV相关炎症无创诊断模型

A Novel Noninvasive Diagnostic Model of HBV-Related Inflammation in Chronic Hepatitis B Virus Infection Patients With Concurrent Nonalcoholic Fatty Liver Disease.

作者信息

Tao Xuemei, Chen Lin, Zhao Youfei, Liu Yonggang, Shi Ruifang, Jiang Bei, Mi Yuqiang, Xu Liang

机构信息

Clinical School of the Second People's Hospital, Tianjin Medical University, Tianjin, China.

Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China.

出版信息

Front Med (Lausanne). 2022 Mar 23;9:862879. doi: 10.3389/fmed.2022.862879. eCollection 2022.

Abstract

BACKGROUND AND AIMS

Patients with chronic hepatitis B virus infection (CBI) with concurrent nonalcoholic fatty liver disease (NAFLD) is becoming increasingly common in clinical practice, and it is quite important to identify the etiology when hepatitis occurs. A noninvasive diagnostic model was constructed to identify patients who need antihepatitis B virus (HBV) therapies [histologic activity index (HAI) ≥ 4] in patients with CBI with concurrent NAFLD by analyzing clinical routine parameters.

APPROACH AND RESULTS

In total, 303 out of 502 patients with CBI with concurrent NAFLD proven by liver biopsy from January 2017 to December 2020 in the Tianjin Second People's Hospital were enrolled and they were divided into the HBV-related inflammation (HBV-I) group (HAI ≥ 4,176 cases) and the non-HBV-I group (HAI < 4,127 cases) according to hepatic pathology. The univariate analysis and multivariate logistic regression analysis were performed on the two groups of patients, and then the HBV-I model of patients with CBI with concurrent NAFLD was constructed. The areas under receiver operating characteristic curves (AUROCs) were used to evaluate the parameters of the regression formula. Another 115 patients with CBI with concurrent NAFLD proven by liver biopsy from January 2021 to January 2022 were enrolled as the validation group. There were some statistical differences in demographic data, biochemical indicators, immune function, thyroid function, virology indicator, and blood routine indicators between the two groups ( < 0.05) and liver stiffness measurement (LSM) in the HBV-I group was significantly higher than those in the non-HBV-I group ( < 0.05). While controlled attenuation parameters (CAP) in the HBV-I group were lower than those in the non-HBV-I group ( < 0.05); (2) We developed a novel model by logistic regression analysis: HBV-I = -0.020 × CAP + 0.424 × LSM + 0.376 × lg (HBV DNA) + 0.049 × aspartate aminotransferase (AST) and the accuracy rate was 82.5%. The area under the receiver operating characteristic (AUROC) is 0.907, the cutoff value is 0.671, the sensitivity is 89.30%, the specificity is 77.80%, the positive predictive value is 90.34%, and the negative predictive value is 81.89%; (3) The AUROC of HBV-I in the validation group was 0.871 and the overall accuracy rate is 86.96%.

CONCLUSION

Our novel model HBV-I [combining CAP, LSM, lg (HBV DNA), and AST] shows promising utility for predicting HBV-I in patients with CBI with concurrent NAFLD with high sensitivity, accuracy, and repeatability, which may contribute to clinical application.

摘要

背景与目的

慢性乙型肝炎病毒感染(CBI)合并非酒精性脂肪性肝病(NAFLD)的患者在临床实践中越来越常见,肝炎发生时明确病因非常重要。通过分析临床常规参数构建了一种非侵入性诊断模型,以识别CBI合并NAFLD患者中需要进行抗乙型肝炎病毒(HBV)治疗[组织学活动指数(HAI)≥4]的患者。

方法与结果

2017年1月至2020年12月在天津市第二人民医院经肝活检证实为CBI合并NAFLD的502例患者中,共纳入303例,根据肝脏病理将其分为HBV相关炎症(HBV-I)组(HAI≥4,176例)和非HBV-I组(HAI<4,127例)。对两组患者进行单因素分析和多因素logistic回归分析,然后构建CBI合并NAFLD患者的HBV-I模型。采用受试者操作特征曲线(ROC)下面积(AUROC)评估回归公式的参数。另外纳入2021年1月至2022年1月经肝活检证实为CBI合并NAFLD的115例患者作为验证组。两组在人口统计学数据、生化指标、免疫功能、甲状腺功能、病毒学指标和血常规指标方面存在一些统计学差异(<0.05),HBV-I组的肝脏硬度值(LSM)显著高于非HBV-I组(<0.05)。而HBV-I组的受控衰减参数(CAP)低于非HBV-I组(<0.05);(2)通过logistic回归分析建立了一个新模型:HBV-I = -0.020×CAP + 0.424×LSM + 0.376×lg(HBV DNA)+ 0.049×天冬氨酸转氨酶(AST),准确率为82.5%。受试者操作特征(ROC)曲线下面积(AUROC)为0.907,截断值为0.671,灵敏度为89.30%,特异度为77.80%,阳性预测值为90.34%,阴性预测值为81.89%;(3)验证组中HBV-I的AUROC为0.871,总体准确率为86.96%。

结论

我们的新模型HBV-I[结合CAP、LSM、lg(HBV DNA)和AST]在预测CBI合并NAFLD患者的HBV-I方面显示出有前景的效用,具有高灵敏度、准确性和可重复性,这可能有助于临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e40/8984271/67ea99680947/fmed-09-862879-g0001.jpg

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