在常规收集的医疗保健数据中识别子痫前期的算法：一项系统评价。
完整摘要 · 中文译文
重要性：子痫前期是全球孕产妇发病和死亡的主要原因之一。随着真实世界数据来源越来越多地用于孕产妇健康监测和药物流行病学研究，有效的算法对于在常规收集的医疗数据中准确识别子痫前期至关重要。 目的：识别和描述在基于人群的医疗数据库中用于确定子痫前期的算法，总结验证指标，并描述所报告患病率估计值的范围。 证据综述：遵循2021年PRISMA指南，在PubMed中检索了2010年1月1日至2025年7月1日发表的研究。纳入使用可复现的基于编码的算法在常规收集的医疗数据中识别子痫前期的观察性研究。筛选和全文审查由两人独立完成，分歧由第三位审查者解决。数据提取重点关注算法定义、编码系统、验证方法和流行病学估计值。共有289项研究符合纳入标准，其中包括13项验证研究。 发现：在纳入的研究中，43.6%使用北欧全国性登记系统，30.1%使用医保理赔数据库，12.1%使用电子健康记录。几乎所有研究均使用了ICD-10编码O14.x；O11.x和O15.x的纳入情况各不相同。验证研究一致显示高特异度（>90%）但低灵敏度（≤80%），反映了仅依赖诊断编码时存在漏判的情况。超过70%的研究未报告确定子痫前期时间窗口的起止点。 所报告的患病率因病例定义和环境的不同介于0.6%至6.6%之间。重度子痫前期的患病率介于0.6%至2.2%之间。包含O11.x、O14.x和O15.x的算法在某些环境中显示出更高的灵敏度。 结论与意义：用于在真实世界数据中识别子痫前期的算法具有高度特异性，但灵敏度参差不齐，编码定义和确定窗口存在显著异质性。标准化报告以及一致纳入O11.x、O14.x和O15.x可提高不同数据来源之间流行病学估计的可比性、可重复性和准确性。高特异性算法可能特别适用于比较安全性研究，而在估计发病率或患病率时应考虑潜在的漏查问题。
完整摘要 · 英文原文
IMPORTANCE: Preeclampsia is a leading cause of maternal morbidity and mortality globally. As real-world data sources are increasingly used for maternal health surveillance and pharmacoepidemiologic studies, valid algorithms are essential for accurately identifying preeclampsia in routinely collected healthcare data. OBJECTIVE: To identify and characterize algorithms used to ascertain preeclampsia in population-based healthcare databases, summarize validation metrics, and describe the range of reported prevalence estimates. EVIDENCE REVIEW: Following PRISMA guidelines of 2021, PubMed was searched for studies published from January 1, 2010, to July 1, 2025. Observational studies using reproducible code-based algorithms to identify preeclampsia in routinely collected healthcare data were included. Screening and full-text review were performed in duplicate, with discrepancies resolved by a third reviewer. Data extraction focused on algorithm definitions, coding systems, validation methods, and epidemiologic estimates. A total of 289 studies met inclusion criteria, including 13 validation studies. FINDINGS: Across included studies, 43.6% used Nordic nationwide registries, 30.1% used claims databases, and 12.1% used electronic health records. ICD-10 O14.x was used in nearly all studies; inclusion of O11.x and O15.x varied. Validation studies consistently demonstrated high specificity (> 90%) but low sensitivity (≤ 80%), reflecting under-ascertainment when relying solely on diagnosis codes. More than 70% of studies did not report start or end points for the time window of ascertainment. Reported prevalence ranged from 0.6% to 6.6% across case definitions and settings. The prevalence of severe preeclampsia ranged from 0.6% to 2.2%. Algorithms including O11.x, O14.x, and O15.x showed improved sensitivity in some settings. CONCLUSIONS AND RELEVANCE: Algorithms for identifying preeclampsia in real-world data are highly specific but variably sensitive, with substantial heterogeneity in coding definitions and ascertainment windows. Standardized reporting and consistent inclusion of O11.x, O14.x, and O15.x may improve comparability, reproducibility, and the accuracy of epidemiologic estimates across data sources. High-specificity algorithms may be particularly useful for comparative safety studies, whereas potential under-ascertainment should be considered when estimating incidence or prevalence.