机器学习算法EVendo的验证,用于预测肝细胞癌中的食道静脉
Jamie O Yang1, Punya Chittajallu2,3, Jihane N Benhammou1,2,4
1UCLA Department of Medicine, Los Angeles, CA, USA.
Digestive diseases and sciences
|June 19, 2024
概括
埃文多得分可以安全地识别患有肝细胞癌 (HCC) 的患者,这些患者不需要食道静脉查. 这种非侵入性工具可以避免不必要的手术,减少医疗保健成本和治疗延迟.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 胃肠病学 胃肠病学
- 在瘤学瘤学.
背景情况:
- 标准的HCC治疗 (阿特佐利祖马布/贝瓦齐祖马布) 会增加出血风险,需要通过EGD进行食道静脉查.
- 治疗前的EGD可能会延迟护理,并造成费用. EVendo评分是为了非侵入性地识别低风险患者而开发的.
研究的目的:
- 为了验证EVendo得分在预测HCC患者中需要治疗的EV (VNT) 存在的准确性.
主要方法:
- 追溯的多中心队列研究,对112名HCC患者进行了117次EGD (9/2004-12/2021).
- 在EGD收集了EVendo分数计算的临床参数.
- EVendo的预测与EGD发现的VNT检测结果进行了比较.
主要成果:
- 33.3%的患者患有VNT.
- EVendo得分显示了高精度,97.4%的灵敏度和96.9%的VNT.负预测值.
结论:
- 在HCC患者中,EVendo得分有效排除了VNT.
- 应用EVendo评分可以安全地推迟大约30%的EGDs,可能降低成本和治疗延迟.
- 需要进一步验证,但EVendo得分显示出优化HCC患者管理的希望.
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