低EHR连续性对影响估计的影响:来自两个EHR-Medicare链接数据库的证据
Yinzhu Jin1, Angela Y Tong1, Richard Wyss1
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Clinical epidemiology
|December 31, 2025
概括
电子健康记录 (EHR) 的连续性会影响研究结果. 更高的EHR连续性减少了对效果估计的偏差,特别是发生率差异,提高了药物流行病学研究的准确性.
科学领域:
- 药学流行病学 药学流行病学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 电子健康记录 (EHR) 对现实世界的证据生成至关重要.
- 电子健康记录数据质量,特别是连续性,可能会影响影响估计效应的准确性.
- 了解EHR连续性的变化带来的偏见对于可靠的研究至关重要.
研究的目的:
- 评估不同级别的EHR连续性如何影响四项药物流行病学研究中的效果估计.
- 为了比较发生率偏差 (IR),发生率差异 (IRD) 和危险比率 (HR) 在不同的EHR连续性.
- 评估缓解偏差的策略,例如排除低连续性患者和倾向得分调整.
主要方法:
- 利用了与医疗保险索赔数据 (2007-2014) 相关的美国EHR系统.
- 在完整人群中计算了IR,IRD和HR,并且排除了25%,50%或75%EHR连续性得分最低的患者.
- 量化偏差在IRD和倾向得分 (PS) 中,经过小数点调整后的HR.
主要成果:
- 仅EHR数据显著低估了真正的发病率 (44.1%-76.2%).
- 排除低连续性患者减少了低估值,但在非用户的比较器设计中加剧了低估值.
- 绝对IRD偏差在PPI与H2RA (0.4%) 和华法林与DOAC (0.7%) 之间很小,但在PPI与非PPI (19.1%) 和OAC与非OAC (7.8%) 之间更大. 相对HR偏差在3.0%至31.5%之间.
结论:
- 更高的EHR连续性减少了发生率低估和偏差,特别是在IRD.
- 倾向性得分的调整和排除低连续性患者可以减轻一些偏差.
- 电子健康记录的不连续性仍然是一个偏差来源,特别是在非用户的比较器中,强调需要平衡连续性和样本大小.
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