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-  2015 

年龄和血清HBsAg、HBV DNA预测慢性乙型肝炎肝组织病理状态的研究

DOI: 10.16118/j.1008-0392.2015.01.012

Keywords: 乙型肝炎表面抗原 乙型肝炎病毒DNA 定量检测 慢性乙型肝炎 肝纤维化 Logistic回归分析
hepatitis B surface antigen hepatitis B virus DNA quantitative detection chronic hepatitis B fibrosis Logistic regression analysis

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Abstract:

目的构建基于年龄和血清HBsAg、HBV DNA诊断慢性乙型肝炎肝组织不同病理状态的Logistic回归模型,优化血清HBsAg、HBV DNA诊断肝组织不同病理状态的效能。方法 经肝组织活检的慢性乙型肝炎患者472例,其中HBeAg阳性279例,HBeAg阴性193例。血清HBsAg和HBeAg采用Abbott Architect I2000及其配套试剂检测,血清HBV DNA采用实时荧光定量PCR检测。统计分析采用SPSS 13.0软件。结果 HBeAg阳性患者的血清HBsAg和HBV DNA与病理学分级和分期均呈显著负相关(P<0.05);HBeAg阴性患者的血清HBV DNA与病理学分级和分期呈显著正相关(P<0.01)。预测HBeAg阳性和阴性患者不同病理状态的回归模型的预测概率诊断不同病理状态的ROC曲线下面积均显著大于对角参考线下面积(P<0.01)。对HBeAg阳性患者,预测进展期纤维化的回归模型的预测概率和血清HBsAg诊断进展期纤维化的最佳截断值分别为≥0.185和≤3.797 log10IU/ml,其对应的灵敏度、特异度、准确度分别为0.886、0.646、0.706和0.800、0.660、0.695;对HBeAg阴性患者,预测显著纤维化的回归模型的预测概率和血清HBV DNA诊断显著纤维化的最佳截断值分别为≥0.603和≥3.095 log10IU/mL,其对应的灵敏度、特异度、准确度分别为0.636、0.720、0.668和0.669、0.653、0.663。结论 基于年龄和血清HBsAg、HBV DNA构建的Logistic回归模型可提升血清HBsAg、HBV DNA诊断肝组织不同病理状态的效能。
Objective To construct a Logistic regression model based on age and serum HBsAg, HBV DNA for prediction of liver tissue pathological states in patients with chronic hepatitis B. MethodsTotal 472 consecutive chronic hepatitis B patients with pathological diagnoses of liver tissues, including 279 HBeAg-positive and 193 HBeAg-negative patients, were enrolled in present study. Serum HBsAg and HBeAg were determined by Abbott Architect I2000 and auxiliary reagents, serum HBV DNA was determined by real-time fluorescence quantitative PCR. SPSS 13.0 software was used for statistical analyses. ResultsIn HBeAg-positive patients, serum HBsAg and HBV DNA were negatively correlated with pathological grading and staging(P<0.05). In HBeAg-negative patients, serum HBV DNA was positively correlated with the pathological grading and staging(P<0.01). In both HBeAg-positive and HBeAg-negative patients, the area under ROC curve of the regression model for predicting different pathological states was significantly larger than that of the diagonal reference(P<0.01). In HBeAg-positive patients, the optimal cut-offs of predictive probability in the regression model for predicting advanced fibrosis and serum HBsAg for diagnosis of advanced fibrosis were ≥0.185 and ≤3.797 log10IU/ml respectively, and the corresponding sensitivities, specificities, accuracies were 0.886,0.646,0.706 and 0.800,0.660,0.695 respectively. In HBeAg-negative patients, the optimal cut-offs of predictive probability in the regression model for predicting significant fibrosis and serum HBV DNA for diagnosis of significant fibrosis were ≥0.603 and ≥3.095 log10IU/ml respectively, and the corresponding sensitivities, specificities, accuracies were 0.636,0.720,0.668 and 0.669,0.653,0.663, respectively. ConclusionLogistic regression models based on age and serum HBsAg, HBV

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