EUROmediCAT signal detection: a systematic method for identifying potential teratogenic medication. (4th August 2016) - Luteijn, Johannes M.; Morris, Joan K.; Garne, Ester; Given, Joanne; de Jong‐van den Berg, Lolkje; Addor, Marie‐Claude; Bakker, Marian; Barisic, Ingeborg; Gatt, Miriam; Klungsoyr, Kari; Latos‐Bielenska, Anna; Lelong, Nathalie; et al.
Abstract : 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.