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通过机器学习重新检查急性损伤中的移植结果
Caroline C Jadlowiec1, Charat Thongprayoon2, Supawadee Suppadungsuk2,3
1Division of Transplant Surgery, Mayo Clinic, Phoenix, Arizona, USA.
Clinical transplantation
|October 5, 2024
概括
从急性损伤 (AKI) 的捐赠者获得的代移植未得到充分利用. 机器学习识别了三个受体集群,具有不同的结果,显示了增加AKI捐赠脏利用的潜力.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 移植 移植 移植 移植
- 机器学习 机器学习
背景情况:
- 来自经历急性损伤 (AKI) 的捐赠者的合移植在移植中仍未得到充分利用.
- 需要优化这些脏的使用,以解决器官短缺问题.
研究的目的:
- 将无监督机器学习 (ML) 应用到从AKI捐赠者获得全移植的移植接受者的集群.
- 识别不同的接受者个人资料,并比较他们的临床结果.
主要方法:
- 在2015-2019年期间,对12,356名已故的捐赠移植接受者进行了共识聚类分析,捐赠者的终端血清肌氨酸≥1.5 mg/dL.
- 分析了确定集群的特征和结果,并与非AKI接受者进行了比较.
主要成果:
- 确定了三个不同的AKI受体群:年轻的再移植患者,首次接受高血压/糖尿病病患者和年长的糖尿病受体.
- 虽然AKI集群显示出更高的延迟移植功能,但长期死亡审查的移植存活率,患者存活率和整体移植存活率与非AKI受体相比较.
- 集群2 (首次接受高血压/糖尿病病患者) 显示出最佳的患者和移植存活结果.
结论:
- 无监督的ML成功地将AKI接受者分为不同的组,具有不同的临床结果.
- 这些发现表明,AKI捐赠脏可以安全地使用,为增加移植率提供了机会.
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