将FDA不良事件报告系统 (FAERS) 描述为改善模式发现的网络
Raechel Davis1, Oanh Dang1, Suranjan De1
1Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20903, USA.
Drug safety
|September 16, 2025
概括
美国食品和药物管理局不良事件报告系统 (FAERS) 的网络分析揭示了药物不良反应的复杂模式. 这项研究描述了FAERS网络的特性,有助于药物安全研究和识别已知的不良事件.
科学领域:
- 药监和药物安全 药监和药物安全
- 网络科学和数据挖掘 网络科学和数据挖掘
- 计算生物学和生物信息学
背景情况:
- 药物安全监测中的不良事件 (AE) 通常以复杂的临床模式呈现.
- 网络分析 (NA) 已经应用于像VAERS这样的系统,但FAERS网络的表征仍然不完整.
研究的目的:
- 描述FDA不良事件报告系统 (FAERS) 的网络属性.
- 为了利用网络特征,在FAERS中改进不良事件模式的发现.
- 提供关于NA在药物安全研究中的应用的见解.
主要方法:
- 使用首选术语 (PT) 和药物作为节点,以互连作为边缘来表示FAERS数据.
- 分析了网络属性,包括度分布,聚类和直径.
- 应用边缘权重算法基于报告的同时出现和集群.
主要成果:
- FAERS网络包括超过20,000个节点 (PT和药物),拥有数百万个相互连接.
- 费尔斯子网络呈现出重尾度分布,高局部聚类和低直径.
- 一个日志正常模型比功率定律更好地描述了度分布.
结论:
- 基于网络的技术成功地确定了已知的不良药物反应和聚类模式.
- FAERS网络特征与其他AE报告系统 (如VAERS) 有相似之处.
- 这个FAERS系统的NA提供了对其网络结构和在药物安全方面的实用性的基本理解.
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