在药监数据库中识别药物不良反应的危险因素
Sofia Attelind1,2, Niclas Eriksson1,3, Anders Sundström2
1Department of Medical Sciences, Clinical Pharmacogenomics, Uppsala University, Uppsala, Sweden.
Pharmacoepidemiology and drug safety
|August 15, 2023
概括
药监数据库中的数据挖掘可以识别药物不良反应 (ADR) 的已知和潜在风险因素,例如直接口服抗凝剂的出血. 这种方法有助于制药监督实践,通过突出显示痴呆和心房等风险进行进一步研究.
科学领域:
- 药物监督 药物监督 药物监督
- 数据挖掘 数据挖掘
- 药品安全 药品安全
背景情况:
- 药监数据库可以识别新的安全信号.
- 这些数据库也可能揭示药物不良反应 (ADR) 的危险因素.
研究的目的:
- 评估药监数据库中的数据挖掘,以识别已知和新出现的副作用风险因素.
- 评估这种方法在药监监督实践中的有用性.
主要方法:
- 在瑞典国家药监数据库上进行了探索性数据挖掘.
- 与直接口服抗凝剂 (DOAC) 相关的出血作为模型.
- 使用后勤回归和Boruta机器学习方法,将出血病例与经历其他严重DOAC相关副作用的对照进行比较.
主要成果:
- 后勤回归确定了13个与DOAC出血相关的因素,11个是以前已知的危险因素.
- 年龄调整后的重要因素包括心律不整,高血压,痴呆症和相互作用的药物.
- 博鲁塔的分析强调了高年龄,心律失常,高血压,心力衰竭和相互作用的药物与出血有关.
结论:
- 数据挖掘有效地确定了DOAC出血的已知风险因素.
- 该方法还揭示了潜在的风险因素,包括痴呆症和心房动.
- 这种方法对于识别需要进一步的药监评估的潜在ADR风险因素可能是有价值的.
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