Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase.

Introduction: The Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and VigiBase are two established databases for safety monitoring of medicinal products, recently complemented with the EudraVigilance Data Analysis System (EVDAS).

Objective: Signals of disproportionate reporting (SDRs) can characterize the reporting profile of a drug, accounting for the distribution of all drugs and all events in the database. This study aims to quantify the redundancy among the three databases when characterized by two disproportionality-based analyses (DPA).

Methods: SDRs for 100 selected products were identified with two sets of thresholds (standard EudraVigilance SDR criteria for all vs Bayesian approach for FAERS and VigiBase). Per product and database, the presence or absence of SDRs was determined and compared. Adverse events were considered at three levels: MedDRA Preferred Term (PT), High Level Term (HLT), and HLT combined with Standardized MedDRA Query (SMQ). Redundancy was measured in terms of recall (SDRs in EVDAS divided by SDRs from any database) and overlap (SDRs in EVDAS and at least one other database, divided by SDRs in EVDAS). Covariates with potential impact on results were explored with linear regression models.

Results: The median overlap between EVDAS and FAERS or VigiBase was 85.0% at the PT level, 94.5% at the HLT level, and 97.7% at the HLT or SMQ level. The corresponding median recall of signals in EVDAS as a percentage of all signals generated in all three databases was 59.4%, 74.1%, and 87.9% at the PT, HLT, and HLT or SMQ levels, respectively. The overlap difference is partially explained by the relative number of EU cases in EudraVigilance and the ratio of EVDAS cases and FAERS cases, presumably due to differences in marketing authorizations, or market penetration in different regions. Products with few cases in EVDAS (< 1500) also display limited recall of signals relative to FAERs/VigiBase. Time-on-market does not predict signal redundancy between the three databases. The choice of the DPA has an expected but somewhat small effect on redundancy.

Conclusions: Organizations typically consider regulatory expectations, operating performance (e.g., positive predictive value), and procedural complexity when selecting databases for signal management. As SDRs can be seen as a proxy of general reporting characteristics identifiable in a systematic screening process, our results indicate that, for most products, these characteristics are largely similar in each of the databases.