公共卫生 公共卫生
Abby L Emdin1, Therese A Stukel2,3, Jennifer Bethell2,4,5
1Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
层次聚类确定了老年痴呆症患者不同的药物处方模式. 虽然中央神经系统活跃集群最初显示较高的入院风险,但在调整其他健康因素后,这种差异减少了.
科学领域:
- 药学流行病学 药学流行病学
- 老年医学 老年医学
- 医疗保健中的机器学习
背景情况:
- 痴呆症患者群体多样化,使药物管理研究复杂化.
- 层次聚类可以识别并发药物治疗中的模式.
- 了解药物模式对于管理老年痴呆症患者的健康风险至关重要.
研究的目的:
- 调查药物处方集群与老年痴呆症患者因跌倒而入院的相关关系.
- 应用无监督机器学习来根据药物使用对痴呆症患者进行分层.
- 评估不同处方药谱对住院风险的影响.
主要方法:
- 使用卫生行政数据分析了一组99,046名被诊断患有痴呆症的老年人.
- 个人被分为六个处方集群,使用等级集群.
- 特定原因的生存模型评估了与落相关的住院危险,并对共变量进行了调整.
主要成果:
- 5%的队列在痴呆症诊断后的一年内经历了与跌倒相关的住院治疗.
- 中枢神经系统 (中枢神经系统) 活跃集群表现出秋季住院的患病率最高 (5.3%).
- 中枢神经系统活性集群显示,与低药使用者相比,跌倒住院的调整风险显著更高 (HR 1.12).
结论:
- 处方集群在痴呆症患者的秋季住院危险中显示出初始差异.
- 这些差异在调整人口统计和临床因素后,在统计学上没有显著意义.
- 该方法提供了一种方法来总结复杂的药物数据,用于未来的药物流行病学研究.
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