陈闪闪, 黄海军. 丙氨酸氨基转移酶≤2倍正常上限值且HBeAg阴性乙型肝炎肝纤维化无创预测模型的建立[J]. 蚌埠医科大学学报, 2024, 49(2): 211-214. DOI: 10.13898/j.cnki.issn.1000-2200.2024.02.016
    引用本文: 陈闪闪, 黄海军. 丙氨酸氨基转移酶≤2倍正常上限值且HBeAg阴性乙型肝炎肝纤维化无创预测模型的建立[J]. 蚌埠医科大学学报, 2024, 49(2): 211-214. DOI: 10.13898/j.cnki.issn.1000-2200.2024.02.016
    CHEN Shanshan, HUANG Haijun. Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal[J]. Journal of Bengbu Medical University, 2024, 49(2): 211-214. DOI: 10.13898/j.cnki.issn.1000-2200.2024.02.016
    Citation: CHEN Shanshan, HUANG Haijun. Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal[J]. Journal of Bengbu Medical University, 2024, 49(2): 211-214. DOI: 10.13898/j.cnki.issn.1000-2200.2024.02.016

    丙氨酸氨基转移酶≤2倍正常上限值且HBeAg阴性乙型肝炎肝纤维化无创预测模型的建立

    Construction of a non-invasive model to predict liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase less than 2 upper limit of normal

    • 摘要:
      目的分析丙氨酸氨基转移酶(ALT)≤2倍正常上限值(2ULN)且HBeAg阴性的慢性乙型肝炎(CHB)肝纤维化的影响因素,并构建无创预测模型,以评估肝纤维化的严重程度。
      方法回顾性分析295例ALT≤2ULN且HBeAg阴性的CHB病人的临床资料。所有病人根据肝穿刺病理结果进行肝纤维化分期,以纤维化分期S≥2作为显著肝纤维化的判别标准。其中肝纤维化轻度组(S≤1)94例,显著组(S≥2)201例。通过多因素logistic回归分析,筛选影响肝纤维化的独立预测因素并构建无创模型,最后通过受试者工作特征曲线下对该模型进行验证,以识别肝纤维化的严重程度。
      结果多因素logistic回归分析显示,天门冬氨酸氨基转移酶、乙肝核心抗体升高可能是肝纤维化的独立预测因素(P < 0.01)。该模型的AUC为0.721(95%CI:0.660~0.782,P < 0.01),诊断显著肝纤维化的敏感性为60.0%,特异性为74.5%。
      结论基于天门冬氨酸氨基转移酶、乙肝核心抗体两项指标构建的无创预测模型对评估CHB肝纤维化的严重程度具有较高的诊断价值。

       

      Abstract:
      ObjectiveTo evaluate influencing factors of liver fibrosis in HBeAg negative hepatitis B with alanine aminotransferase (ALT) less than 2 upper limit of normal(ULN)and establish the non-invasive prediction model to assess the severity of liver fibrosis.
      MethodsThe clinical data of 295 patients in HBeAg negative CHB patients with ALT≤2ULN were retrospectively analyzed.The degree of liver fibrosis S≥2 was taken as the discriminant criterion for significant liver fibrosis according to the pathological results of liver puncture.There were 94 cases in the mild group of liver fibrosis (S≤1) and 201 cases in the significant group (S≥2).The independent predictors of liver fibrosis were screened by multivariate logistic regression analysis and non-invasive model was constructed.Finally, the model was evaluated by area under the receiver operating characteristic curve to identify the severity of liver fibrosis.
      ResultsMultivariate logistic regression analysis showed that aspartate aminotransferase and hepatitis B core antibody were the independent predictors of liver fibrosis (P < 0.01).The AUC of this model was 0.721(95%CI: 0.660-0.782, P < 0.01).The sensitivity and specificity for the diagnosis of significant liver fibrosis were 60.0% and 74.5%.
      ConclusionsThe non-invasive prediction model based on the two indicators of aspartate aminotransferase and hepatitis B core antibody has high diagnostic value for evaluating the severity of liver fibrosis in CHB.

       

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