Identifying signals of potentially harmful medications in pregnancy: use of the double false discovery rate method to adjust for multiple testing

Signal Detection in EUROmediCAT: Identification and Evaluation of Medication-Congenital Anomaly Associations and Use of VigiBase as a Complementary Source of Reference

INTRODUCTION

Knowledge on the safety of medication use during pregnancy is often sparse. Pregnant women are generally excluded from clinical trials, and there is a dependence on post-marketing surveillance to identify teratogenic medications.

AIMS

This study aimed to identify signals of potentially teratogenic medications using EUROmediCAT registry data on medication exposure in pregnancies with a congenital anomaly, and to investigate the use of VigiBase reports of adverse events of medications in the evaluation of these signals.

METHODS

Signals of medication-congenital anomaly associations were identified in EUROmediCAT (21,636 congenital anomaly cases with 32,619 medication exposures), then investigated in a subset of VigiBase (45,749 cases and 165,121 exposures), by reviewing statistical reporting patterns and VigiBase case reports. Evidence from the literature and quantitative and qualitative aspects of both datasets were considered before recommending signals as warranting further independent investigation.

RESULTS

EUROmediCAT analysis identified 49 signals of medication-congenital anomaly associations. Incorporating investigation in VigiBase and the literature, these were categorised as follows: four non-specific medications; 11 likely due to maternal disease; 11 well-established teratogens; two reviewed in previous EUROmediCAT studies with limited additional evidence; and 13 with insufficient basis for recommending follow-up. Independent investigations are recommended for eight signals: pregnen (4) derivatives with limb reduction; nitrofuran derivatives with cleft palate and patent ductus arteriosus; salicylic acid and derivatives with atresia or stenosis of other parts of the small intestine and tetralogy of Fallot; carbamazepine with atrioventricular septal defect and severe congenital heart defect; and selective beta-2-adrenoreceptor agonists with posterior urethral valve and/or prune belly.

CONCLUSION

EUROmediCAT data should continue to be used for signal detection, accompanied by information from VigiBase and review of the existing literature to prioritise signals for further independent evaluation.

Bayesian hierarchical methods in the detection of potentially teratogenic first-trimester medications

PURPOSE

Bayesian hierarchical models (BHMs) have been used to identify adverse drug reactions, allowing information sharing amongst adverse reactions and drugs expected to have similar properties. This study evaluated the use of BHMs in the routine signal detection analyses of potential first-trimester teratogens, where these models have not previously been applied.

METHODS

Data on 15 058 malformed foetuses exposed to first trimester medications (1995-2011) from 13 European congenital anomaly (CA) registries were analysed. The proportion of each CA in women taking a specific medication was compared with the proportion of that CA in all other women in the dataset (55 CAs × 523 medications). BHMs were grouped by either medications or CAs or by both simultaneously, and the results compared with analysing each medication-CA combination separately and adjusting for multiplicity using a double false discovery rate (FDR) procedure. The proportions of "high-risk" medications (medications which have been shown to carry a moderate to high risk of foetal malformations) identified as potential signals were compared, as well as the total number of potential signals requiring follow up (the effective workload).

RESULTS

BHMs identified more high-risk medications than the double FDR method, but the effective workload was larger. A BHM grouping both medications and CAs, for example, identified 23% of high-risk medications compared with 14% by the double FDR; however, there was an increase from 16 to 71 potential signals requiring follow up.

CONCLUSION

For comparable effective workloads, BHMs did not outperform the double FDR, which is comparatively straightforward to implement and is therefore recommended for continued use in teratogenic signal detection analyses.

Identifying signals of potentially harmful medications in pregnancy: use of the double false discovery rate method to adjust for multiple testing

AIMS

Surveillance of medication use in pregnancy is essential to identify associations between first trimester medications and congenital anomalies (CAs). Medications in the same Anatomical Therapeutic Chemical classes may have similar effects. We aimed to use this information to improve the detection of potential teratogens in CA surveillance data.

METHODS

Data on 15 058 malformed fetuses with first trimester medication exposures from 1995-2011 were available from EUROmediCAT, a network of European CA registries. For each medication-CA combination, the proportion of the CA in fetuses with the medication was compared to the proportion of the CA in all other fetuses in the dataset. The Australian classification system was used to identify high-risk medications in order to compare two methods of controlling the false discovery rate (FDR): a single FDR applied across all combinations, and a double FDR incorporating groupings of medications.

RESULTS

There were 28 765 potential combinations (523 medications × 55 CAs) for analysis. An FDR cut-off of 50% resulted in a reasonable effective workload, for which single FDR gave rise to eight medication signals (three high-risk medications) and double FDR 50% identified 16 signals (six high-risk). Over a range of FDR cut-offs, double FDR identified more high-risk medications as signals, for comparable effective workloads.

CONCLUSIONS

The double FDR method appears to improve the detection of potential teratogens in comparison to the single FDR, while maintaining a low risk of false positives. Use of double FDR is recommended in routine signal detection analyses of CA data.