Algorithms to Identify Major Congenital Malformations in Routinely Collected Healthcare Data: A Systematic Review

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.