对于肝硬化合并高危食管静脉曲张（High-risk Varices, HRV）的患者，使用无创标志物进行早期诊断和干预对于降低出血事件发生率具有关键临床价值。在HRV无创筛查领域，基于常规临床参数的预测模型，如APRI、Forns index、Lok index、Fib-4、Fibro index等1-5，诊断效能欠佳，曲线下面积（Area Under the Curve, AUC）约为0.6左右3，难以满足临床需求。

多项研究表明，血小板（Platelet, PLT）和肝脏硬度值（Liver Stiffness Measurement, LSM）是HRV诊断的关键独立预测因子6-13。基于此证据基础，Baveno VI共识14提出了诊断HRV的Baveno VI标准（若患者LSM<20 kPa且PLT>150×109/L，则认为患者无HRV），但该标准的胃镜豁免率仅为25%左右15,16，临床应用受限。

近年来，脾脏硬度值（Spleen Stiffness Measurement, SSM）作为HRV诊断因子展现出良好价值17-19，Baveno VII共识提出了诊断HRV的Baveno VII标准20（若患者不符合Baveno VI标准，但符合SSM<40 kPa，仍认为患者没有HRV）。尽管Baveno VII标准显著提高胃镜豁免率，但需要依赖专用脾脏硬度测量设备，增加医疗成本及技术普及难度。

8月2日，第十一届北京肝病学术会议顺利召开。会上，解放军总医院第五医学中心福军亮教授作了题为“肝硬化合并高危食管静脉曲张早期诊断研究进展”的专题报告。报告中，福教授重点分享了其主持开展的一项全国性、多中心、大样本的临床数据研究，利用回顾性队列（n=3104）和前瞻性（n=1383）队列进行模型建立和模型验证，基于机器学习算法提出了针对HRV诊断效能更佳的诊断模型。

该项研究提出的机器学习模型，在两个队列中的AUC分别为0.948、0.898。在胃镜豁免率方面，该模型在回顾性队列（55.54% vs. 19.07%，P=0.01）和前瞻性队列（56.25% vs. 28.92%，P=0.01）中均显著优于Baveno VI标准。相较于Baveno VII标准，该机器学习模型在不增加SSM专用检查设备的情况下，实现约55%的患者豁免胃镜检查，与Baveno VII标准报道的诊断效能相似，提升了HRV筛查的经济性和可及性。

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（来源：《国际肝病》编辑部）

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