FIB-4plus Score:一种基于机器学习的新型工具,用于查补偿性肝硬化中的高风险静脉瘤 (CHESS2004):国际多中心研究
Bingtian Dong1,2, Ruiling He3,4, Shenghong Ju5
1Liver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China; Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University; State Key Laboratory of Digital Medical Engineering, Nanjing, China.
一个新的FIB-4plus评分准确地预测了补偿性肝硬化患者的高风险食道静脉瘤 (EV). 这种工具结合了FIB-4,肝硬度和硬度测量,以更好地管理患者.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 胃肠病学 胃肠病学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 消化管胃腺镜 (EGD) 查食道静脉 (EV) 是常见的,但许多患者缺乏显著的发现.
- 在补偿性肝硬化中识别高风险的EV (HRV) 对于及时干预和预防并发症至关重要.
研究的目的:
- 开发和验证一种新的评分系统,FIB-4plus,用于预测补偿性肝硬化患者的HRV.
- 结合非侵入性标记,如FIB-4分数,肝硬度测量 (LSM) 和硬度测量 (SSM) 以提高诊断准确度.
主要方法:
- 一项国际性的多中心队列研究,涉及502名补偿性肝硬化患者.
- 利用机器学习算法 (逻辑回归和极端梯度提升) 集成FIB-4组件,LSM和SSM.
- 外部验证是在独立的患者队列上进行的.
主要成果:
- XGBoost-FIB-4plus得分显示了HRV的优异预测性能,在培训队列中达到0.927的AUROC,在验证队列中达到高值.
- 在预测EV和HRV方面,FIB-4plus得分显著超过单个参数 (FIB-4,LSM,SSM,PLT).
- 为了模型的可解释性,使用了沙普利添加式解释 (SHAP).
结论:
- FIB-4plus评分是一个有价值的,非侵入性工具,用于预测EV和HRV在患者的补偿性肝硬化.
- 这一分数可以帮助临床医生优化患者管理策略并改善结果.
- 进一步的研究可以探索其在不同肝硬化患者群体中的实用性.
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