在例行收集的医疗保健数据中识别主要先天性形的算法:系统性审查
Melanie H Jacobson1, Meritxell Sabidó2, Ana Sofia Afonso3
1Global Epidemiology, Johnson & Johnson, 1125 Trenton-Harbourton Road, Titusville, NJ, 08560, USA. Mjacob17@its.jnj.com.
Drug safety
|September 13, 2025
概括
这一系统性审查确定了医疗保健数据中主要先天性形 (MCM) 的算法. 这些发现有助于研究人员在使用例行收集的数据进行怀孕安全研究.
科学领域:
- 药学流行病学 药学流行病学
- 生殖健康 生殖健康
- 公共卫生 公共卫生
背景情况:
- 主要先天性形 (MCMs) 是怀孕安全研究的关键结果.
- 准确识别MCM对于可靠的证据生成至关重要.
研究的目的:
- 系统地审查和总结用于在例行收集的医疗保健数据中识别MCM的算法.
- 涵盖来自美国,加拿大和欧洲的数据来源.
主要方法:
- 从2010年1月1日到2025年4月11日进行了系统的文献审查.
- 包括MCM,医疗保健数据和孕妇/婴儿的搜索术语.
- 进行重复研究审查和数据提取.
主要成果:
- 包括289项研究,其中一半以上来自欧洲,主要使用国家注册数据.
- 算法因数据源,地理位置,编码系统和记录利用而有所不同.
- 大多数验证研究 (70.4%) 使用声明/EHR数据,积极的预测值往往超过70%.
结论:
- 这是对常规医疗保健数据中MCM识别算法的首次全面审查.
- 这些发现支持研究人员为怀孕安全制药流行病学提供强有力的证据.
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