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EUROmediCAT signal detection: a systematic method for identifying potential teratogenic medication
Author(s) -
Luteijn Johannes M.,
Morris Joan K.,
Garne Ester,
Given Joanne,
Jongvan den Berg Lolkje,
Addor MarieClaude,
Bakker Marian,
Barisic Ingeborg,
Gatt Miriam,
Klungsoyr Kari,
LatosBielenska Anna,
Lelong Nathalie,
Nelen Vera,
Neville Amanda,
O'Mahony Mary,
Pierini Anna,
Tucker David,
Walle Hermien,
Wiesel Awi,
Loane Maria,
Dolk Helen
Publication year - 2016
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.13056
Subject(s) - medicine , odds ratio , pregnancy , teratology , pharmacovigilance , pharmacoepidemiology , drug , bioinformatics , pharmacology , fetus , medical prescription , biology , genetics
Aims Information about medication safety in pregnancy is inadequate. We aimed to develop a signal detection methodology to routinely identify unusual associations between medications and congenital anomalies using data collected by 15 European congenital anomaly registries. Methods EUROmediCAT database data for 14 950 malformed foetuses/babies with first trimester medication exposures in 1995–2011 were analyzed. The odds of a specific medication exposure (coded according to chemical substance or subgroup) for a specific anomaly were compared with the odds of that exposure for all other anomalies for 40 385 medication anomaly combinations in the data. Simes multiple testing procedure with a 50% false discovery rate (FDR) identified associations least likely to be due to chance and those associations with more than two cases with the exposure and the anomaly were selected for further investigation. The methodology was evaluated by considering the detection of well‐known teratogens. Results The most common exposures were genitourinary system medications and sex hormones (35.2%), nervous system medications (28.0%) and anti‐infectives for systemic use (25.7%). Fifty‐two specific medication anomaly associations were identified. After discarding 10 overlapping and three protective associations, 39 associations were selected for further investigation. These associations included 16 which concerned well established teratogens, valproic acid (2) and maternal diabetes represented by use of insulin (14). Conclusions Medication exposure data in the EUROmediCAT central database can be analyzed systematically to determine a manageable set of associations for validation and then testing in independent datasets. Detection of teratogens depends on frequency of exposure, level of risk and teratogenic specificity.