Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm.

Abstract

Pharmacovigilance involves monitoring of drugs and their adverse drug reactions (ADRs) and is essential for their safety post-marketing. Because of the different types and structures of medical databases, several previous surveillance studies have analyzed only one database. In the present study, we extracted potential drug-ADR pairs from electronic health record (EHR) data using the MetaNurse algorithm and analyzed them using the Korean Adverse Event Reporting System (KAERS) database for systematic validation. The Medical Dictionary for Regulatory Activities (MedDRA) and World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) were mapped for signal detection. We used the Side Effect Resource (SIDER) database to select 2663 drug-ADR pairs to investigate unknown drug-induced ADRs. The reporting odds ratio (ROR) value was calculated for the drug-exposed and non-exposed groups of drug-ADR pairs, and 19 potential pairs showed significant signals. Appropriate terminology systems and criteria are needed to handle diverse medical databases.