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机器学习共识集群识别的高度敏感的脏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.
Medicina (Kaunas, Lithuania)
|May 27, 2023
概括
机器学习在高度敏感的脏移植接受者中发现了两个不同的患者群体 (PRA ≥98%). 一组显示了较差的移植存活率和更高的排斥率,为个性化移植策略提供了信息.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 移植免疫学 移植免疫学
- 医学中的数据科学.
背景情况:
- 移植接受者具有面板反应性抗体 (PRA) ≥98%的临床结果较差,尽管分配优先.
- 识别该人口中的子组对于定制的管理策略至关重要.
- 脆弱的患者群体需要精确的风险分层,以改善移植后的成功.
研究的目的:
- 使用无监督机器学习对高度敏感的移植接受者进行分类 (PRA ≥98%).
- 识别具有不同临床结果的不同子组.
- 为个性化管理策略提供信息,以改善患者和移植存活率.
主要方法:
- 对器官采购和移植网络 (OPTN) /器官共享联合网络 (UNOS) 数据库 (2010-2019) 的分析.
- 对7458名脏移植患者的共识集群分析,移植前PRA≥98%.
- 基于接受者,捐赠者和移植特征的鉴定集群之间的移植后结果的比较.
主要成果:
- 确定了两个不同的高度敏感的移植接受者的集群.
- 集群1:年轻,男性,先前的移植史,较少的糖尿病病;较低的死亡审查移植存活率,较高的急性排斥.
- 集群2:老年人,女性,首次移植;可比的患者存活率,更好的死亡审查移植存活率,较低的急性排斥.
结论:
- 无监督机器学习有效地将高度敏感的移植患者分为两组临床上不同的子组.
- 这些子组在移植后的结果不同,特别是在移植后的生存和急性排斥方面.
- 了解这些独特的集群可以指导开发个性化护理策略,以改善这一脆弱人群的结果.
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