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在QResearch数据库中建立怀孕登记册的方法

Andrew Jhl Snelling¹, Emma Copland^1,2, Winnie X Mei²

¹Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

Communications medicine

|December 23, 2025

概括

一个新的算法使用电子健康记录创建了一个怀孕登记册,在COVID-19大流行期间促进对孕产妇健康的重要研究. 这种有价值的资源捕获了分娩和妊娠损失数据,以更好地理解结果.

科学领域:

流行病学流行病学
医疗信息学医疗信息学
生殖健康生殖健康

背景情况:

电子健康记录 (EHR) 对于怀孕研究至关重要,因为怀孕个体的代表性不足.
结合初级和二级护理数据,增强了怀孕研究能力.
这项研究详细介绍了在COVID-19流行病的紧急阶段在QResearch数据库内统一妊娠队列的算法.

研究的目的:

开发和描述一个算法,在QResearch数据库中创建统一的怀孕队列.
通过整合从初级和二级保健数据来促进与怀孕相关的研究.
建立一个全面的资源来研究怀孕的结果和特定时期的事件.

主要方法:

将国家初级保健记录 (QResearch®) 与医院病例统计数据 (HES) 联系起来.
包括15-50岁的女性,在2020年12月至2022年9月期间记录了怀孕结果.
采用三阶段算法来识别妊娠期,并使用组合或单个数据源推导开始日期.

主要成果:

该登记册包括266,758名妇女和279,027名怀孕.
83.4%的怀孕导致分娩 (99.5%的活产),16.6%的怀孕是流产.
怀孕流产率因种族而异,在加勒比地区最高 (23.1%) 和巴基斯坦 (13.9%) 群体最低.
数据来源包括HES产妇记录 (82.4%),初级保健 (10.6%),HES入院 (3.4%) 和HES程序 (3.6%).

结论:

QResearch® 怀孕登记册是未来研究的宝贵资源.
开发的方法适用于构建跨不同时间段的队列.
这种方法提供了关于怀孕结果和事件的全面数据集.

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