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A scoring model predicts hepatitis B e antigen seroconversion in chronic hepatitis B patients treated with nucleos(t)ide analogs: real-world clinical practice

机译:评分模型预测接受核苷酸(t)ide类似物治疗的慢性乙型肝炎患者的乙型肝炎e抗原血清转换:现实世界中的临床实践

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Aim: This study developed and validated a non-invasive scoring model to predict 1-year hepatitis B e antigen (HBeAg) seroconversion in response to nucleos(t)ide analog (NA) treatment in NA-naive patients with HBeAg-positive chronic hepatitis B (CHB). Methods: Baseline data from 1014 patients visiting the outpatient and inpatient clinics of Beijing Ditan Hospital, Capital Medical University, China between October 2008 and April 2015 were included. These patients received NAs for HBeAg-positive CHB. The patients were assigned randomly to the derivation (n=710) and validation (n=304) cohorts in a 7:3 ratio. A prediction scoring model was established based on univariate and multivariate Cox proportional hazards regression analyses to identify independent prediction factors. In the derivation cohort, the odds ratio of the predictors were converted to integer risk scores by rounding the quotient from dividing the odds ratio, and the final score was the sum of these values. The predictive accuracy of the scoring model was further assessed using Harrell's concordance index (C-index). Results: The 1-year cumulative HBeAg seroconversion rates were 11.83% and 8.55% in the derivation and validation cohorts, respectively. In the derivation cohort, baseline pretreatment alanine aminotransferase (ALT), gamma-glutamyl transpeptidase (GGT), globulin (GLO), and quantitative HBeAg (qHBeAg) levels were independently associated with HBeAg seroconversion and were included in the scoring system. The model had good discrimination in the derivation and validation cohorts (C-index=0.750, 95% confidence interval 0.694-0.806 and C-index=0.776, 95% confidence interval 0.698-0.855, respectively). The prediction scores ranged from 0 to 4; scores of 0-1 and 2-4 identified patients with lower and higher levels of HBeAg seroconversion, respectively. Kaplan-Meier analysis was used to determine the 1-year cumulative HBeAg seroconversion rates in the two groups (scores of 0-1 and 2-4) of the primary cohort, and log-rank tests revealed a significant difference (4.87% vs. 20.9%, p<0.0001). Conclusions: The 1-year prediction scoring model based on baseline levels of ALT, GGT, GLO, and qHBeAg offered a reliable predictive value for the response to NA therapy in a Chinese cohort.
机译:目的:本研究开发并验证了一种非侵入性评分模型,该模型可预测NA初治型HBeAg阳性慢性肝炎患者对核苷类似物(NA)治疗后的1年乙型肝炎e抗原(HBeAg)血清转化B(CHB)。方法:包括2008年10月至2015年4月在中​​国首都医科大学附属北京地坛医院门诊和住院门诊就诊的1014例患者的基线数据。这些患者接受HBeAg阳性CHB的NAs。患者以7:3的比例随机分配至派生组(n = 710)和验证组(n = 304)。基于单变量和多变量Cox比例风险回归分析,建立预测评分模型,以识别独立的预测因素。在派生队列中,通过除以比值比对商进行四舍五入,将预测变量的比值比转换为整数风险分数,最终分数是这些值的总和。使用Harrell一致性指数(C-index)进一步评估了评分模型的预测准确性。结果:在派生组和验证组中,1年累计HBeAg血清转化率分别为11.83%和8.55%。在派生队列中,基线预处理丙氨酸转氨酶(ALT),γ-谷氨酰转肽酶(GGT),球蛋白(GLO)和定量HBeAg(qHBeAg)水平与HBeAg血清转化独立相关,并包含在评分系统中。该模型在派生和验证队列中具有良好的辨别力(分别为C-index = 0.750,95%置信区间0.694-0.806和C-index = 0.776,95%置信区间0.698-0.855)。预测分数介于0到4之间; 0-1分和2-4分分别确定了HBeAg血清转化水平较低和较高的患者。 Kaplan-Meier分析用于确定主要队列的两组(0-1和2-4评分)的1年累积HBeAg血清转化率,对数秩检验显示出显着差异(4.87%vs. 20.9%,p <0.0001)。结论:基于ALT,GGT,GLO和qHBeAg基线水平的1年预测评分模型为中国人群NA治疗的反应提供了可靠的预测价值。

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