开发一种新型的诺莫格拉姆模型,用于预测慢性乙型肝炎病毒携带者的肝脏组织学显著变化
Na-Ling Kang1,2, Ya-Hong Gao3, Meng-Xin Lin4
1Department of Hepatology, Clinical Research Center for Liver and Intestinal Diseases of Fujian Province, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fujian, China.
Journal of medical virology
|July 12, 2023
概括
一种新的名谱模型,HBGP,准确地识别了慢性乙型肝炎病毒 (HBV) 携带者具有正常ALT水平的肝脏组织学显著变化. 这个工具有助于决定HBV患者的抗病毒治疗.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 病毒学 病毒学
- 生物统计学 生物统计学
背景情况:
- 慢性乙型肝炎病毒 (HBV) 感染影响全球数百万人.
- 一些HBV携带者具有正常的氨酸转胺酶 (ALT) 水平,表现出显著的肝脏组织学变化 (SLHC).
- 准确的,非侵入性识别SLHC对于及时的治疗决定至关重要.
研究的目的:
- 开发和验证一种非侵入性名录模型,用于识别慢性HBV携带者与正常ALT.的SLHC.
- 评估模型在ALT的不同正常上限 (ULN) 的性能.
主要方法:
- 分析了732名慢性HBV携带者的培训队列和277名慢性HBV携带者的外部验证队列.
- 使用了后勤回归和最小绝对收缩和选择操作员 (LASSO) 分析.
- 使用乙型肝炎表面抗原,玛-氨基转酶和血小板计数,构建了一个名ogram模型,HBGP.
主要成果:
- 在HBGP nomogram显示强大的预测性能SLHC,与AUC的0.866在培训队列和0.885在验证队列.
- 在不同的ALT ULN层中观察到高诊断值 (AUC范围为0.853至0.868).
- 在识别SLHC时,HBGP的表现优于现有的预测指标.
结论:
- 乙型肝炎基因组图 (HBGP nomogram) 是一种可靠的,非侵入性的工具,用于预测慢性乙型肝炎病毒携带者与正常ALT的SLHC.
- 这个模型可以帮助临床医生做出关于启动抗病毒治疗的明智决策.
- 在不同人群中进一步验证可能会提高其临床实用性.
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