Jacobson Melanie H, Sabidó Meritxell, Afonso Ana Sofia, Ajao Adebola, Alghamdi Eman A, Andrade Susan E, Bennett Dimitri, Kharat Vineetkumar, Kürzinger Marie-Laure, Le Noan-Lainé Maryline, Mølgaard-Nielsen Ditte, Murray Gayle, Rivero-Ferrer Elena, Lopez-Leon Sandra
Global Epidemiology, Johnson & Johnson, 1125 Trenton-Harbourton Road, Titusville, NJ, 08560, USA.
Patient Focused Real World Evidence, Merck Healthcare KGaA, Darmstadt, Germany.
Drug Saf. 2025 Sep 13. doi: 10.1007/s40264-025-01606-w.
Major congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies.
This study aimed to identify and summarize algorithms used to identify MCMs in routinely collected healthcare data sources in the USA, Canada, and Europe by conducting a systematic literature review.
We developed a search strategy to identify studies containing algorithms for MCMs from January 1, 2010, to April 11, 2025. Search terms included those related to MCMs as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants. Study review and data extraction was conducted in duplicate using a standardized data collection form.
Among the initially identified 2242 studies, 974 were selected for full-text review. Of these, 70.3% were excluded, leaving 289 studies. Over half (58.1%) of the included studies were from Europe, predominantly from Nordic countries using national register data (N = 135; 80.4%). Studies using claims (18.0%) or hospital discharge data (16.3%) were also common. Although there was heterogeneity in the timing of MCM assessment, 55.7% of studies collected MCMs through the infant's first year of life. Overall, algorithms varied across data source type and geography in the codes specified, rules, utilization of maternal versus infant records, and coding system. There were 27 (9.3%) validation studies, 70.4% of which were based on claims and/or electronic health record data only. Most had positive predictive values >70%, though this varied according to MCM type or anatomical site.
We provide the first comprehensive systematic literature review of algorithms used to identify MCMs in routinely collected healthcare data, aiding researchers in their ability to generate reliable evidence in pregnancy safety pharmacoepidemiology.
重大先天性畸形（MCMs）是妊娠安全性研究中主要关注的结果。
本研究旨在通过系统的文献综述，识别并总结美国、加拿大和欧洲在常规收集的医疗保健数据源中用于识别MCMs的算法。
我们制定了一项检索策略，以识别2010年1月1日至2025年4月11日期间包含MCMs算法的研究。检索词包括与作为结果的MCMs、常规收集的医疗保健数据、可能纳入算法的流行病学设计以及孕妇和/或婴儿相关的词汇。使用标准化数据收集表对研究进行重复审查和数据提取。
在最初识别的2242项研究中，974项被选进行全文审查。其中，70.3%被排除，剩下289项研究。超过一半（58.1%）的纳入研究来自欧洲，主要来自使用国家登记数据的北欧国家（N = 135；80.4%）。使用索赔数据（18.0%）或医院出院数据（16.3%）的研究也很常见。尽管MCM评估的时间存在异质性，但55.7%的研究在婴儿出生后的第一年收集MCMs。总体而言，算法在指定代码、规则、母婴记录的使用以及编码系统方面因数据源类型和地理位置而异。有27项（9.3%）验证研究，其中70.4%仅基于索赔和/或电子健康记录数据。大多数研究的阳性预测值>70%，不过这因MCM类型或解剖部位而异。
我们首次对用于在常规收集的医疗保健数据中识别MCMs的算法进行了全面的系统文献综述，有助于研究人员在妊娠安全性药物流行病学中生成可靠证据。
