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通过机器学习共识集群识别的患有糖尿病的已故捐赠者的移植接受者之间的差异
Charat Thongprayoon1, Jing Miao1, Caroline C Jadlowiec2
1Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Journal of personalized medicine
|July 29, 2023
概括
机器学习确定了接受糖尿病捐赠移植的三组患者. 一组面临更高的移植失败和死亡风险,突出了更好的器官利用机会.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 移植 移植 移植 移植
- 机器学习在医学中的应用
背景情况:
- 来自糖尿病捐赠者的已故捐赠者移植的临床结果不一致.
- 在糖尿病捐赠者的移植受体人群中存在很高的异质性.
研究的目的:
- 使用无监督机器学习,对糖尿病患者已故捐赠移植的接受者进行集群.
- 确定具有较低结果和相关变量高风险的子组.
主要方法:
- 达成共识的集群分析对7876名糖尿病已故捐赠移植的接受者 (2010-2019) 进行了分析.
- 分析使用了来自OPTN/UNOS数据库的接受者,捐赠者和移植相关特征.
- 根据移植后的结果确定了三个不同的集群并进行了比较.
主要成果:
- 集群1 (n=2903) 包含具有更多并发症的老年接受者,接受来自老年,扩展标准捐赠者 (ECD) 的脏,其脏捐赠者概况指数 (KDPI) 高.
- 集群2 (n=687) 包括具有高面板反应性抗体 (PRA) 的年轻再移植患者,从年轻的非ECD捐赠者获得脏,HLA不匹配低.
- 集群3 (n=2903) 由第一次移植接受者组成,接受的来自年轻的非ECD捐赠者.
- 与集群3相比,集群1显示了较高的初级非功能,延迟的移植功能,患者死亡和死亡审查的移植失败率.
结论:
- 无监督的机器学习有效地将糖尿病捐赠者移植接受者划分为三个不同的集群,结果各不相同.
- 研究结果表明,有机会提高糖尿病捐赠者的高KDPI脏的利用率,特别是在具有生存限制并发病症的接受者中 (集群1).
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