对DIC广泛同意队列的评估
Marvin O Kampf1, Hans-Ulrich Prokosch2, Christian Gulden2
1Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
对比医院和广泛的同意患者队列,揭示了人口统计学和临床偏见. 研究人员应该考虑联合数据分析,以减轻这些偏见,当广泛的同意不是普遍获得时.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 临床数据分析 临床数据分析
背景情况:
- 电子健康记录 (EHR) 对研究有价值,但需要患者的同意.
- 获得研究数据使用的广泛同意 (BC) 可以引入患者选择偏见.
- 了解这些偏见对于准确的临床数据分析至关重要.
研究的目的:
- 为了比较广泛的同意 (BC) 队列与医院 (HOSP) 队列的代表性.
- 为了确定具有和没有广泛同意的患者之间的人口和临床差异.
- 为研究环境中临床数据分析提供最佳实践信息.
主要方法:
- 在一个单一的学术医疗中心进行回顾性观察性研究.
- 对伪名化的人口和临床数据 (ICD-10诊断) 的分析.
- 从2021年3月到2023年12月,一个大型医院队列 (n=98,564) 与一个较小的广泛同意队列 (n=1,678) 的比较.
主要成果:
- 与医院队列相比,在广泛的同意队列中观察到临床和人口统计特征的显著偏差.
- 逐步实施获得同意的过程有助于这些观察到的偏见.
- 广泛的同意队列并不完全代表整体医院患者群体.
结论:
- 广泛的同意过程引入了选择偏见,影响了研究队伍的代表性.
- 研究人员在利用广泛的同意数据时应该意识到这些偏见.
- 建议采用联合数据分析方法,以克服与集中广泛同意模型相关的局限性.
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