行政医疗索赔数据库中妊娠识别与孕周推算:一个更新、透明且可跨库移植的算法
Identification and dating of pregnancies in administrative healthcare claims databases: An updated, transparent, and transportable algorithm
摘要
该研究将既往开发的妊娠识别算法更新至 ICD-10-CM 编码体系,并转换为标准化格式以便跨数据库移植;随后在美国三个医保索赔数据库(2000–2025 年,覆盖商业保险与公共保险人群)中评估其实现特征。共识别出约 580 万、300 万(两个商业数据库)和 230 万(Medicaid 多州数据库)例妊娠。各库中活产占比 76–83%、流产/人工流产 15–23%、异位妊娠约 1%、死胎≤0.5%;Medicaid 库活产比例更高。孕周推算方式随编码时代显著变化:使用孕龄记录的比例从 ICD-9-CM 时代的 4–9% 升至 ICD-10-CM 时代的 87–92%。研究提示,数据库特征与编码转换会系统性影响妊娠识别与孕周推算,为可重复的妊娠药物流行病学研究提供方法学参考。
原文摘要
Studies using routinely collected healthcare data are essential for understanding the benefit-risk profile of medications in pregnancy. However, reproducibility is limited by the lack of shared pregnancy identification algorithms, and generalizability is constrained by their development primarily in commercial insurance databases. A previously developed pregnancy identification algorithm was updated for ICD-10-CM and translated into a standardized format to support transport across diverse databases. Algorithm implementation characteristics were evaluated in three US health insurance claims databases (2000-2025), including commercially and publicly insured populations. Overall, 5.8 million and 3.0 million pregnancies were identified in two commercial databases (Merative™ and Optum) and 2.3 million pregnancies in Merative™ MarketScan® Multi-State Medicaid Database (MDCD). Across databases, live birth prevalence ranged from 76-83%, miscarriage/abortion 15-23%, ectopic pregnancy 1%, and stillbirth ≤0.5%, with a higher proportion of live births in MDCD. Pregnancy dating methods varied substantially across coding eras: use of gestational age records increased from 4-9% in the ICD-9-CM era to 87-92% during the ICD-10-CM era, with greater use in MDCD. Using a standardized algorithm, this study demonstrates how database characteristics and coding transitions influence pregnancy identification and dating in healthcare data, highlighting methodological considerations for reproducible pregnancy research across data sources.
推荐理由:样本量巨大、跨三个数据库验证、算法透明可移植,是药物孕期安全与妊娠队列研究的重要方法学基础;但不涉及 ART 或胚胎实验室操作,与辅助生殖临床的直接关联有限。
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