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预测移植患者的不良结果,使用基于剪波弹性图形的可解释模型
Jieying Wang1,2, Jiayi Yan1,3, Tianyi Zhang1
1Department of Nephrology, Molecular Cell Laboratory for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Ren Ji Hospital, Uremia Diagnosis and Treatment Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Kidney diseases (Basel, Switzerland)
|July 3, 2025
概括
剪波弹性图 (SWE) 可以预测移植失败. 将SWE与实验室测试相结合,可以提高预测的准确性,为全移植健康提供一种非侵入性监测工具.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 脏纤维化是脏移植中的一个关键因素.
- 需要使用非侵入性方法来监测移植接受者.
- 剪波弹性图 (SWE) 显示了评估纤维化的潜力.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测移植受体脏移植的变化.
- 为了评估SWE特征在这个人群中的预测性能.
主要方法:
- 一项前性队列研究包括396名脏移植接受者.
- 进行了剪波弹性图 (SWE) 检查.
- 使用SWE特征和实验室数据开发了一个XGBoost模型,以预测40%的eGFR下降或末期脏疾病的复合结果.
主要成果:
- 采用SWE特征的XGBoost模型实现了高预测性能 (在训练中AUC为0.870,在验证中为0.868).
- 在SWE上,较高的髓髓或皮质组织度与更差的预后有关.
- 升高的骨髓SWE (>10kPa) 独立预测了不良结果 (aOR2.68).
结论:
- 将SWE参数与实验室数据相结合,可以显著提高预测全移植功能下降的可能性.
- 使用非侵入性SWE的可解释的XGBoost模型显示了指导移植患者监测的前景.
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