Case‐malformed signal detection and prioritisation using EUROmediCAT data for pharmacovigilance in pregnancy
Hannah Johnson, Helen Dolk, Maria Loane, Christine Damase‐Michel, Joanne Given, Hedvig Nordeng, Jorieke E. H. Bergman, Iain Carey, Elly Den Hond, Ester Garne, Florence Rouget, Lea Bruneau, Isabelle Monier, Anke Rissmann, Mary O'Mahony, Miriam Gatt, Renée Lutke, Joanna Sichitiu, Elisa Ballardini, Alessio Coi, Clara Cavero Carbonell, David Tucker, Joan MorrisAim
Many women take medications during pregnancy. However, the risk to the fetus from most medications is uncertain. Congenital anomalies are one of the leading causes of infant death and contribute to long‐term disability. Signal detection methods can be used to systematically identify possible medication–anomaly associations that require further investigation.
Methods
Data on first trimester medication exposures in pregnancies with a congenital anomaly reported to 14 EUROmediCAT registries with a birth year of 2005–2018 were analysed. Case‐malformed disproportionality analysis identified medication–anomaly signals using a disproportionality signal detection method. Identified signals were then compared to previous EUROmediCAT signal detection studies and ranked based on a proxy for the severity of impact at a population level using the number of estimated excess congenital anomaly cases within the exposed population and the average additional time in hospital over the first year of life. Generalized linear mixed models were used to assess confounding by birth year and registry within the top 20 ranked signals.
Results
1611 medication–outcome pairs with at least three observations were analysed, and 153 signals for 63 different medications were identified. Of the top 20 ranked signals, 10 (involving nine medications) were prioritized for further independent investigation.
Conclusion
The signals identified here are hypothesis forming only. Independent studies that adequately account for confounding are subsequently needed to evaluate if the identified signals are causal. Further investigation of signals with low population impact, but high potential individual risk is also recommended.