基于济南不良事件报告系统数据库的神经保护剂安全警报信号的挖掘和评估:一项回顾性研究
Yu-Yao Guan1, Jing Yang1, Ying-Mei Qi2
1Department of Pharmacy, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
数据挖掘确定了神经保护剂的安全信号,揭示了人口统计和药物使用的不良事件的变化. 这种分析有助于减少与这些药物相关的临床风险.
科学领域:
- 药物监督管理
- 药物安全
- 神经药理学
背景情况:
- 神经保护剂正在调查中,但监管漏洞和各种证据引发了安全问题.
- 营销后监测对于了解这些药物的实际安全性非常重要.
研究的目的:
- 通过数据挖掘研究神经保护剂在市场批准后的安全警告信号.
- 建立安全临床使用神经保护剂的基础.
主要方法:
- 使用了济南不良事件 (AE) 报告系统 (2000年1月至2022年3月).
- 分析患者数据,报告和常见的副作用,包括子组和敏感性分析.
- 应用了四种不相称性分析方法 (PRR,ROR,BCPNN,MHRA) 来检测安全信号,并将结果与中国,美国和日本的药品标签进行了比较.
主要成果:
- 在168,314个AE中,有2,094个AE与神经保护剂有关.
- 在不同年龄组,性别和同时使用药物的风险信号中观察到显著的差异.
- 检测到多个安全信号,在中国和日本,大量的副作用没有被列在药品标签上.
结论:
- 济南AE报告系统有效地挖掘了上市后神经保护剂的警告信号.
- 这种数据挖掘方法可以帮助减少与神经保护剂临床应用相关的风险.
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