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基于响应导向治疗策略的预测模型,预测B型肝炎e抗原血清转化在慢性乙型肝炎中,接受了 peginterferon-alfa 治疗

Pei-Xin Zhang¹, Xiao-Wei Zheng², Ya-Fei Zhang¹

¹Department of Infectious Diseases, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui Province, China.

World journal of hepatology

|April 5, 2024

概括

这项研究开发了一种简单的评分模型,用于预测慢性乙型肝炎 (CHB) 患者的乙型肝炎e抗原 (HBeAg) 血清转化. 该模型基于响应导向疗法 (RGT),准确预测HBeAg阳性CHB患者接受干扰素-α (PEG-IFNα) 的治疗结果.

科学领域:

肝病学肝病学是一种肝病学.
病毒学病毒学
临床医学临床医学

背景情况:

在接受核胺模拟治疗的HBeAg阳性慢性乙型肝炎 (CHB) 患者中,对乙型肝炎e抗原 (HBeAg) 血清转换的预测模型有限.
准确预测HBeAg血清转化对于优化CHB管理中的治疗策略至关重要.

研究的目的:

建立一个简单的评分模型,用响应引导治疗 (RGT) 策略来预测HBeAg血清转化.
预测乙型肝炎表面抗原 (HBsAg) 清除与HBeAg血清转化结合.

主要方法:

一组75名先前接受治疗的HBeAg阳性CHB患者接受了52周的干扰素-α (PEG-IFNα),随后进行了24周的随访.
后勤回归分析在基线,第12周和第24周确定了关键预测因素,以建立预测模型.
基于不同时间点对HBsAg和HBeAg水平的最佳切断值,开发了一个评分系统.

主要成果:

对HBeAg血清转化最重要的预测因素是基线,第12周和第24周的HBsAg和HBeAg水平.
基线,第12周或第24周的得分为2与高响应率 (81.8-82.4%) 和HBsAg清除率 (40.0-54.5%) 相相关.
相反,得分为0与显著较低的响应和HBsAg清除率有关.

结论:

使用RGT策略,成功建立了实用的预测模型和诊断-治疗过程.
该模型有助于预测HBeAg和HBsAg血清转化在接受PEG-IFNα治疗的HBeAg阳性CHB患者.
基于RGT的评分系统为个性化CHB管理提供了有价值的工具.

关键词:

慢性乙型肝炎慢性乙型肝炎乙型肝炎e抗原阳性.干扰素-阿尔法预测模型的预测模型.响应导向治疗策略响应导向治疗策略

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