在药监督病例报告数据库中发现怀孕暴露:对VigiBase怀孕算法的全面评估
Lovisa Sandberg1, Sara Hedfors Vidlin2, Levente K-Pápai2
1Uppsala Monitoring Centre, Uppsala, Sweden. lovisa.sandberg@who-umc.org.
Drug safety
|June 23, 2025
概括
一个新的算法有效地识别了VigiBase中的与怀孕相关的不良事件,改善了怀孕期间药物安全监测. 该工具通过准确地检索关键的妊娠暴露数据来增强药物监测.
科学领域:
- 药物监督和药物安全研究.
- 医疗信息学和数据挖掘.
- 生殖健康和制药科学.
背景情况:
- 在怀孕期间对药物安全性的营销后监测非常重要,因为预审批数据有限.
- 从药监数据库中检索与怀孕有关的病例报告是具有挑战性的,因为缺乏具体指标.
- 开发了一个算法来识别VigiBase中的怀孕暴露报告,这是世卫组织的全球不良事件数据库.
研究的目的:
- 评估和描述VigiBase怀孕算法的性能.
- 评估算法在识别与怀孕有关的不良事件报告中的准确性.
- 将算法的性能与标准的MedDRA查询 (SMQ) 进行比较.
主要方法:
- 使用ICH E2B格式的结构化数据元素开发了一个基于规则的算法.
- 评估使用了三个数据集 (完整数据集,低样本数据集和随机数据集),并为黄金标准使用手动注释.
- 计算了包括回忆 (灵敏度) 和精度 (正预测值) 在内的性能指标,并与SMQ基线进行了比较.
主要成果:
- 该算法在Downsampled数据集中实现了75%的回忆,在符合ICH E2B规范的报告中增加到91%.
- 随机数据集的准确度为92%,主要的错误阳性原因包括产后症状.
- 与算法相比,SMQ基线显示回忆力 (62%) 和精度 (74%) 较低.
结论:
- VigiBase 怀孕算法在促进与怀孕相关的药物监督方面表现出强大的性能.
- 这项研究为未来的研究提供了有价值的基准,用于识别怀孕暴露.
- 强调全球协调报告怀孕暴露标准.
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