ArticleDrug safety2024
Identification of Pregnancy Adverse Drug Reactions in Pharmacovigilance Reporting Systems: A Novel Algorithm Developed in EudraVigilance.
Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Causal Inference Tools for Pharmacovigilance: Using Causal Graphs to Identify and Address Biases in Disproportionality Analysis.Drug safety · 2026Article
- From Pharmacovigilance Signals to Mechanistic Phenotypes: Integrating ADMET, PK/PD, and Network Context to Interpret Antiviral Safety in Pregnancy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Charting and Sidestepping the Pitfalls of Disproportionality Analysis.Drug safety · 2026Review
- Impact of Side Effects on 5-Year Adherence to Adjuvant Endocrine Therapy in Women with Breast Cancer: An Integrative Review.International journal of women's health · 2026Review
- Janus Kinase Inhibitors During Pregnancy and Adverse Drug Reactions: A Pharmacovigilance Disproportionality Analysis in VigiBase.Clinical and translational science · 2025Article
- Suitability of Japanese Medical Databases for Studies on Infant Outcomes After Maternal Drug Exposure: An Evaluation Based on Core Data Elements.Pharmacoepidemiology and drug safety · 2025Article
- Article
- Uncovering Pregnancy Exposures in Pharmacovigilance Case Report Databases: A Comprehensive Evaluation of the VigiBase Pregnancy Algorithm.Drug safety · 2025Article
- Pregabalin Safety in Pregnancy: A Disproportionality Analysis of VigiBase Spontaneous Reporting System.Pharmaceuticals (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThere is a need to strengthen the evidence base regarding medication use during pregnancy and to facilitate the early detection of safety signals. EudraVigilance (EV) serves as the primary system for managing and analysing information concerning suspected adverse drug reactions (ADRs) within the European Economic Area. Despite its various functionalities, the current format for electronic submissions of safety reports lacks a specific data element indicating medicine exposure during pregnancy.
objectiveThis paper aims to address the limitations of existing approaches by developing a rule-based algorithm in EV that more reliably identifies cases that are truly representative of an ADR during pregnancy.
methodsThe study utilised the standardised MedDRA query (SMQ) 'Pregnancy and neonatal topics' (PNT) as a benchmark for comparison. Recognising that the SMQ PNT also retrieves healthy pregnancy outcomes, contraceptive failure, failed abortifacients as well as ADRs not associated with pregnancy, a novel algorithm was tailored to improve the accuracy of identifying suspected ADRs occurring during pregnancy.
resultsUpon testing, the algorithm demonstrated superior performance, correctly predicting 90% of cases reporting an ADR during pregnancy, compared to 54% achieved by the SMQ PNT. The implementation of the algorithm in EV led to the retrieval of 202,426 cases.
conclusionThe development and successful testing of the novel algorithm represents a step forward in pregnancy-specific signal detection in EV. Because signals associated with pregnancy may be diluted in a large database such as EV, this study lays the groundwork for future research to evaluate the effectiveness of disproportionality methods on a more refined subset of pregnancy-related ADR reports.
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