> For the complete documentation index, see [llms.txt](https://support.who-umc.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://support.who-umc.org/vigilyze/algorithms-used-in-vigilyze/pregnancy-algorithm.md).
# Pregnancy algorithm
The VigiBase pregnancy algorithm is a rule-based method developed to identify pregnancy-related reports. More specifically, it uses structured data elements to identify reports of maternal exposure to a medicinal product before or during pregnancy, with or without adverse events in the pregnant person or the prenatally exposed. It does not flag patients over 50 years, paternal exposures, or lactation reports.
The algorithm consists of two main steps, as shown in Figure 1. First, reports that are considered unlikely to be pregnancy-related are ruled out. Second, if none of the rule-out criteria are met, reports are flagged if they meet any of the rule-in criteria.
Figure 1. The VigiBase pregnancy algorithm
According to UMC’s evaluation of the algorithm based on around 8,000 manually annotated reports, the algorithm is expected to identify 75% of all pregnancy cases in VigiBase (i.e. recall/sensitivity). When restricted to reports transmitted in the E2B format, this proportion increases to 91%. In addition, 92% of the reports identified by the algorithm as pregnancy cases are correctly classified (i.e. precision/positive predictive value. This proportion is unchanged when restricted to reports transmitted in the E2B format.
#### Get to know more
* [Comprehensive evaluation of algorithm - Research poster](https://who-umc.org/media/j2cpykuc/pregnancy_poster_digital.pdf)
* [Uncovering pregnancy cases in VigiBase - Uppsala Reports](https://uppsalareports.org/articles/uncovering-pregnancy-cases-in-vigibase/)
* Sandberg, L., Vidlin, S.H., K-Pápai, L. *et al.* Uncovering Pregnancy Exposures in Pharmacovigilance Case Report Databases: A Comprehensive Evaluation of the VigiBase Pregnancy Algorithm. *Drug Saf* (2025).
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