聚乙二醇干扰素α治疗HBeAg阳性慢性乙型肝炎血清学转换预测模型的建立及验证

发布时间：2019-01-17 15:32

【摘要】：目的：建立聚乙二醇干扰素α治疗HBeAg阳性慢性乙型肝炎血清学转换的预测模型并验证其预测价值。方法：连续收集自本中心确诊为HBeAg阳性CHB患者的基线、第12周、16周、24周和48周相关血清学指标。将病例随机分为建模组和验证组。在建模组中，对建模组的各因素与HBeAg血清学转换的显著性关系进行单因素分析，把有显著性关系的预测因素用二元logistic回归分析验证HBeAg血清学转换的独立预测因素。选取独立预测因素及回归系数建立预测模型。采取约登指数值最大为最佳临界点以区分是否在48周内发生HBeAg血清学转换。在验证组中用受试者工作曲线（ROC曲线）分析方法验证模型的诊断价值。结果：共有129例CHB患者纳入本研究。随机抽取90例作为建模组，其余39例作为验证组。对各预测因素与发生HBeAg血清学转换进行单因素分析，结果显示，基线HBV-DNA（P=0.017）、HBeAg12（P0.001）、HBV-DNA12（P=0.007）、HBeAg16（P0.001）、HBV-DNA16（P=0.034）、HBeAg24（P0.001）、HBV-DNA24（P=0.010）、S/D（P=0.037）、S/D12（P=0.007）、S/D24（P=0.044）、ΔE24/HBeAg （P=0.001）均与发生HBeAg血清学转换存在相关性。应用二元Logistic回归分析法分析相关指标，得出HBeAg16（P=0.009）、HBeAg24(P0.001)为独立预测因素。构建一个HBeAg血清学转换预测的模型S：S=0.806+0.659ln（HBeAg16）-1.297ln（HBeAg24）。把预测模型运用于验证组HBeAg血清学转换预测通过ROC曲线进行分析，得出AUC为0.873，95%可信区间为0.764~0.983。取约登指数最大值为预测的最佳临界值（S=-0.917），根据这个临界值进行预测，敏感性为100%，特异性为67.9%，阳性预测值为55.0%，阴性预测值为100%。结论：由第16周和第24周HBeAg水平构建的预测模型对PegIFNα治疗HBeAg阳性CHB患者血清学转换率有较好的预测准确性。
[Abstract]:Objective: To establish a predictive model for seroconversion of HBeAg-positive chronic hepatitis B and to verify its predictive value. Methods: The baseline, 12-week, 16-week, 24-week and 48-week serological markers of HBeAg-positive CHB patients were collected. The cases were randomly divided into the modeling group and the verification group. In the module under construction, the single-factor analysis of the relationship between the factors of the modeling group and the seroconversion of HBeAg was carried out, and the independent prediction factors of the seroconversion of HBeAg were verified by the binary logistic regression analysis. and the independent prediction factors and the regression coefficients are selected to establish a prediction model. The most preferred critical point was taken to distinguish whether HBeAg seroconversion occurred within 48 weeks. The diagnostic value of the model was verified by the subject's work curve (ROC curve) in the validation group. Results: A total of 129 CHB patients were included in this study. 90 cases were randomly selected as the modeling group and the remaining 39 cases were used as the verification group. The results showed that the HBV-DNA (P = 0.017), BAg12 (P0.001), HBV-DN12 (P = 0.7), AAg16 (P0.001), HBV-DN16 (P = 0.034), AAg24 (P0.001), HBV-DN24 (P = 0. 010), S/ D (P = 0.037), S/ D12 (P = 0. 007), S/ D24 (P = 0.044), AE24/ HBeAg (P = 0. 001) were all related to the seroconversion of HBeAg. The correlation index was analyzed by the two-way logistic regression analysis method, and the independent predictors were obtained from the AAg16 (P = 0. 009) and the AAg24 (P0.001). A model S: S = 0.806 + 0.659ln (SupAg16)-1.297ln (SupAg24) for HBeAg seroconversion prediction was constructed. The prediction model was used in the seroconversion prediction of HBeAg in the validation group, and the ROC curve was analyzed to obtain the AUC of 0.873 and the confidence interval of 95% was 0.764-0.9883. The predicted optimal threshold (S =-0.917) was used to predict the maximum value of the denden index (S =-0.917). The sensitivity was 100%, the specificity was 6.7. 9%, the positive predictive value was 55. 0%, and the negative predictive value was 100%. Conclusion: The predictive model constructed by the levels of HBeAg at week 16 and week 24 has good predictive accuracy for the seroconversion rate of PegIFN-treated HBeAg-positive CHB patients.
【学位授予单位】：福建医科大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R512.62

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