应用一种创新的数据挖掘方法,以实现老年人安全的多药房实践
Yi Shi1, Chien-Wei Chiang2, Kathleen T Unroe3,4
1Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA.
Drug safety
|November 7, 2023
概括
这项研究使用数据挖掘在老年人中发现高风险药物组合,确定更安全的替代品来减少不良药物事件 (ADEs). 这些知识支持更安全的多药实践.
科学领域:
- 老年医学 老年医学
- 药理学 药理学是指药理学的学科.
- 数据科学数据科学数据科学
背景情况:
- 多药在老年人中很普遍,增加了药物不良事件 (ADEs) 的风险.
- 目前用于识别药物组合引起的ADE风险的方法有限.
- 发现高风险药物组合对于老年人安全药物管理至关重要.
研究的目的:
- 应用创新的数据挖掘方法来识别高风险,高阶药物组合.
- 为已识别的高风险组合发现替代性,低风险的药物组合.
- 为了提高老年人多药药的安全性.
主要方法:
- 使用Medicare和MarketScan数据,对65岁以上的成年人进行紧急诊所 (ED) 访问.
- 使用国际疾病分类 (ICD) 代码来识别与特定药物类 (抗凝血剂,抗糖尿病药物,阿片类药物) 相关的ADE.
- 应用了混合药物计数反应模型和基于治疗类的挖矿,以确定高风险和替代低风险药物组合.
主要成果:
- 分析了840万个ED访问记录,确定了5213种与ADE风险增加相关的高阶药物组合 (FDR<0.01).
- 在类似的治疗类中发现了1904个高风险,高阶药物组合与潜在的低风险替代组合.
结论:
- 一种新的数据挖掘技术有效地识别了与ADE风险增加相关的高阶药物组合.
- 高风险药物组合经常在同一治疗类中具有更安全的替代品,为多药学提供了实际的解决方案.
相关概念视频
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00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
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01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
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01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
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01:19Pharmacovigilance
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01:02Prescription, Nonprescription and Orphan Drugs
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