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解决在英语CPRD GOLD怀孕登记册中识别怀孕的不确定性:使用工作示例进行的一项方法研究

Yangmei Li¹, Jennifer J Kurinczuk¹, Fiona Alderdice¹

¹NIHR Policy Research Unit in Maternal and Neonatal Health and Care, National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, Oxford, OX3 7LF, United Kingdom.

International journal of population data science

|March 5, 2025

概括

清理电子健康记录可以改善临床实践研究数据链接怀孕登记册中的怀孕识别. 这种方法提高了关于怀孕前护理研究的数据质量.

科学领域:

医疗信息学医疗信息学
生殖健康生殖健康
数据科学数据科学数据科学

背景情况:

电子健康记录 (EHR) 对怀孕研究至关重要.
临床实践研究数据链接 (CPRD) 怀孕登记册 (PR) 识别了怀孕,但包括不确定的病例.
准确的怀孕识别对于可靠的健康结果研究至关重要.

研究的目的:

提出一种方法,以减少在CPRD GOLD PR数据中识别怀孕的不确定性.
为了证明该方法在怀孕前护理提供研究中的应用.
提高EHR数据的准确性和实用性,用于流行病学研究.

主要方法:

利用CPRD母亲婴儿链接 (MBL) 和产科医院情节统计 (HES) 来增强CPRD公关数据.
开发并应用一个清洁和组合算法来识别怀孕.
根据数据质量标准创建了三个数据群 ("按提供", "衍生", "严格衍生").

主要成果:

该算法通过整合医院数据,确定了额外的22,270例怀孕.
冲突,不确定或非当代怀孕记录被最小化.
"严格衍生"人群显示,先前存在的疾病的患病率更高,怀孕前的护理增加,暴露结果记录有所改善.

结论:

为了准确的怀孕识别,CPRD PR数据需要进行清洁.
拟议的方法提供了一种务实的方法,利用现有的和链接的记录来增强妊娠数据.
研究人员可以调整这种方法来满足改善数据质量的特定研究要求.

关键词:

临床实践研究数据链接 (CPRD)医院情节统计 (HES) 产妇母亲婴儿链接链接怀孕登记怀孕登记怀孕登记怀孕登记电子健康记录是电子健康记录.电子医疗记录电子医疗记录方法学研究研究方法学研究.在怀孕期间,怀孕.常规国家卫生服务 (NHS) 数据.

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