SynthesisDrug safety2026
Algorithms to Identify Major Congenital Malformations in Routinely Collected Healthcare Data: A Systematic Review.
Synthesis in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Exploring the Impact of Missing Death Data on Identification of Congenital Malformations in Insurance Claims Data.Paediatric and perinatal epidemiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMajor congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies.
objectiveThis 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.
methodsWe 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.
resultsAmong 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.
conclusionWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.