使用k-means集群来识别急性缺血性中风的新型表型,并开发其Clinlabomics模型
Yao Jiang1, Yingqiang Dang1, Qian Wu1
1Laboratory Medicine Center, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Frontiers in neurology
|April 11, 2024
概括
这项研究确定了急性缺血性中风 (AIS) 的三个新型表型,并开发了针对个性化治疗的Clinlabomics模型. 这些模型准确地分类AIS表型,改善患者护理策略.
科学领域:
- 神经学 神经学
- 基因组学就是基因组学.
- 个性化医疗是个性化的医疗.
背景情况:
- 急性缺血性中风 (AIS) 是一种具有显著异质性的复杂疾病.
- 目前的治疗策略可能无法完全解决AIS的各种潜在原因.
- 识别不同的患者亚组对于开发向疗法至关重要.
研究的目的:
- 为了分层AIS的异质性.
- 使用无监督集群识别新型患者表型.
- 为个性化AIS治疗开发Clinlabomics模型.
主要方法:
- 对AIS和非AIS住院患者的回顾性分析.
- K-意味着聚类以确定AIS表型.
- 变量选择的拉索回归.
- 支持矢量机 (SVM) 分类器用于Clinlabomics模型开发.
- 模型性能使用AUC,精度,灵敏度和特异性进行评估.
主要成果:
- 在909名患者中确定了三种不同的AIS表型:表型1 (肥胖,脂质升高),表型2 (异常的离子水平) 和表型3 (炎症,器官功能障碍).
- 现型特征在507名AIS患者的单独队列中得到验证.
- 用于表型1和2的clinlabomics模型使用SVM显示出高性能 (AUC>0.97,精度>0.93).
结论:
- 确定了三种新的AIS表型,反映了不同的潜在病理生理机制.
- 对这些表型成功开发了clinlabomics模型.
- 这些模型支持为AIS患者推进个性化治疗方法.
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