使用共识聚类来识别慢性病的不同亚型和相关的死亡风险
Yi Qin1, Liping Xuan2, Zhe Wu1
1Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Scientific reports
|December 2, 2024
概括
数据驱动的集群确定了四个不同的慢性病 (CKD) 亚组. 这些CKD亚型显示出不同的死亡风险,为准确医学在患者护理中的方法铺平了道路.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 慢性病 (CKD) 是一种多方面的疾病,其原因和结果各不相同.
- 识别特定的CKD患者亚组对于理解不同死亡风险至关重要.
研究的目的:
- 用数据驱动的集群方法将慢性病 (CKD) 分类为不同的亚型.
- 调查已识别的CKD亚组与死亡风险之间的关联.
主要方法:
- 在美国国家健康和营养检查调查 (NHANES) (1999-2018) 中,从6526名参与者中对45个基线特征进行了雇佣的无监督共识聚类.
- 检查了CKD亚组与所有原因,心血管疾病,癌症和其他原因死亡率之间的关联.
主要成果:
- 根据基线特征将6526名患有CKD的个体分为四个不同的群体.
- 从集群1 (有利的标志物) 到集群4 (最差的标志物) 观察到增加死亡风险的梯度 (所有原因,心血管,其他原因).
- 敏感性分析证实了基线功能和死亡风险相似的子组内的患者异质性.
结论:
- 达成共识的集群成功地确定了具有明显不同的死亡风险的CKD分组.
- 这些发现支持通过进一步检查CKD患者异质性的精密医学的进步.
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