通过集群和网络分析来确定多病症心力衰竭患者的子组
Catarina Martins1,2, Bernardo Neves3,4,5, Andreia Sofia Teixeira6,7
1Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.
BMC medical informatics and decision making
|April 15, 2024
概括
本研究使用电子健康记录在心力衰竭 (HF) 中确定了不同的患者群体. 这些集群揭示了多种多重疾病模式,影响了住院风险和改善了患者分层.
科学领域:
- 临床信息学 临床信息学
- 心脏病学 心脏病学
- 公共卫生 公共卫生
背景情况:
- 多种疾病,多种慢性疾病的存在,使患者护理和医疗保健系统复杂化.
- 了解心力衰竭 (HF) 患者的多病症是有限的,阻碍了有效的治疗和风险分层.
- 电子健康记录 (EHR) 为表征复杂患者群体提供了丰富的数据来源.
研究的目的:
- 开发和介绍一个工作流程,用于识别和描述使用EHR数据的多病症HF患者.
- 根据其多病症概况,探索高风暴患者群体内的异质性.
- 调查不同多病态模式对临床结果 (如住院患者) 的预后影响.
主要方法:
- 在3745名心力衰竭患者的队列中进行了集群分析.
- 使用的数据包括人口统计,并发症,实验室值和来自EHR的药物处方.
- 分析的目的是根据患者的多病症概况来确定不同的患者子组.
主要成果:
- 在HF患者中确定了四个不同的患者群.
- 这些群体在多发病概况上表现出显著的差异.
- 鉴定到的群体显示出不同的预后影响,特别是在未计划入院的情况下.
结论:
- 在多病性方面,HF患者群体高度异质.
- 电子病历数据可以有效地用于描述高频率内的患者子组.
- 改进的患者亚组表征有可能在心力衰竭管理中增强临床风险分层.
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