利用机器学习来开发,验证和预后评估MASH风险评分:来自多队列研究的见解
Bicheng Ye1, Yuming Teng2, Qianqian Chen3
1Liver Disease Center of Integrated Traditional Chinese and Western Medicine, Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China.
NPJ digital medicine
|December 8, 2025
概括
一个新的血液测试,MASH风险得分,准确地预测了代谢功能障碍相关的脂肪肝炎 (MASH) 和相关并发症的风险. 这种非侵入性工具有助于早期检测和患者分层,以改善结果.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 生物标志物发现发现
- 机器学习在医学中的应用
背景情况:
- 与代谢功能障碍相关的脂肪肝炎 (MASH) 显著增加与肝脏相关的死亡风险.
- 对于MASH预测和风险分层的有效非侵入性生物标志物目前是有限的.
研究的目的:
- 开发和验证一种新的,基于血液的MASH的非侵入性诊断工具.
- 评估MASH风险评分在预测MASH和相关不良结果方面的表现.
主要方法:
- 利用十个机器学习算法,从218名参与者的常规临床和生物化学数据中得出MASH风险得分.
- 在多个独立队伍中验证了得分,共计26,256人.
- 与已建立的纤维化指数 (FIB-4,APRI,AAR,NFS) 相比,MASH风险评分的表现.
主要成果:
- 马什风险得分显示了强大的诊断性能 (AUC从0.791到0.806不等).
- 它独立地预测了MASH,并且表现优于现有的非侵入性得分.
- 在一个大量的预后队列 (390,574人) 中,高MASH风险发现肝脏死亡率,MASH,肝硬化,高血型肝硬化,ASCVD死亡率和全因死亡率的风险显著增加.
结论:
- 马什风险评分是一个经过验证,用户友好和非侵入性工具,用于MASH的早期检测.
- 它可以有效地分层风险,并预测MASH患者的不良结果.
- 这一分数可以指导临床管理,改善患者的预后.
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