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一种基于双弹性图的新型模型,用于查与乙型肝炎病毒相关的肝硬化中的高风险静脉瘤

Xing Hu¹, Min Chen², Ying Zheng¹

¹Center of Ultrasound and Functional Diagnosis, Beijing Youan Hospital, Capital Medical University, Beijing, China.

Frontiers in medicine

|March 12, 2026

概括

一个新的DELU模型准确地识别了乙型肝硬化患者的高风险食道静脉瘤,减少了不必要的内镜检查. 这种非侵入性测试为管理肝病并发症提供了更安全的替代方案.

科学领域:

肝病学肝病学是一种肝病学.
医学诊断医学诊断医学诊断
机器学习在医学中的应用

背景情况:

与乙型肝炎病毒 (HBV) 相关的肝硬化中的性出血是一种严重并发症,死亡率高.
有效的非侵入性测试 (NIT) 对于评估食道静脉 (EV) 严重程度和出血风险至关重要.
目前的NIT在准确排除高风险变节 (HRV) 方面存在局限性.

研究的目的:

开发和验证一个强大的非侵入性模型,以安全地排除HBV相关肝硬化患者的HRV.
将新模型的诊断性能和临床实用性与已建立的NIT进行比较.
使用机器学习识别HRV的预测因素.

主要方法:

在五个中心招募328名HBV相关肝硬化患者.
在培训队列 (n=184) 上使用后勤回归和机器学习开发DELU模型.
在独立队列 (n=144) 上对DELU模型进行外部验证,并与Baveno VI标准,扩展的Baveno VI标准,血小板-脏比率和RESIST-HCV标准进行比较.

主要成果:

包含血小板计数,ALT,,PVT,IDM和肝硬度的DELU模型实现了0.822 (训练) 和0.779 (验证) 的AUROC.
在诊断准确性方面,Delu显著超过了RESIST和Baveno VI标准.
德鲁显示了高的内镜节省率 (高达20.7%),并在各种子组中保持了表现.

结论:

德鲁模型是一种可靠和安全的非侵入性工具,用于排除HBV相关肝硬化中的HRV.
德鲁提供卓越的诊断准确性和显著的潜力,以减少内镜程序.
当弹性图无法使用时,RESIST是一个可行的替代方案.

关键词:

肝硬化是肝硬化的一种疾病.双弹性弹性学双弹性弹性学乙型肝炎 B 型肝炎高风险的静脉.非侵入性试验试验是非侵入性试验.

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