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

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.

[1]  Weida Tong,et al.  Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA , 2019, BMC Bioinformatics.

[2]  L. Härmark,et al.  Pharmacovigilance: methods, recent developments and future perspectives , 2008, European Journal of Clinical Pharmacology.

[3]  Richard Platt,et al.  Active drug safety surveillance: a tool to improve public health , 2008, Pharmacoepidemiology and drug safety.

[4]  Prakash M. Nadkarni,et al.  Drug safety surveillance using de-identified EMR and claims data: issues and challenges , 2010, J. Am. Medical Informatics Assoc..

[5]  Rae Woong Park,et al.  Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance , 2019, Drug Safety.

[6]  Ji-Hwan Bae,et al.  Machine Learning for Detection of Safety Signals From Spontaneous Reporting System Data: Example of Nivolumab and Docetaxel , 2021, Frontiers in Pharmacology.

[7]  Hua Xu,et al.  Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records , 2013, J. Am. Medical Informatics Assoc..

[8]  Antoni F. Z. Wisniewski,et al.  Investigating Overlap in Signals from EVDAS, FAERS, and VigiBase® , 2020, Drug Safety.

[9]  Expression of drug transporters in human kidney: impact of sex, age, and ethnicity , 2015, Biology of Sex Differences.

[10]  L. Hazell,et al.  Under-Reporting of Adverse Drug Reactions , 2006, Drug safety.

[11]  Scott Boyer,et al.  Gathering and Exploring Scientific Knowledge in Pharmacovigilance , 2013, PloS one.

[12]  Ju-Young Shin,et al.  Signal Detection for Cardiovascular Adverse Events of DPP-4 Inhibitors Using the Korea Adverse Event Reporting System Database, 2008–2016 , 2019, Yonsei medical journal.

[13]  F. Domergue,et al.  Comparison Between Paediatric and Adult Suspected Adverse Drug Reactions Reported to the European Medicines Agency: Implications for Pharmacovigilance , 2014, Pediatric Drugs.

[14]  D. Thompson,et al.  Effect of concurrent medications on cisplatin-induced nephrotoxicity in patients with head and neck cancer , 2006, Anti-cancer drugs.

[15]  Joongyub Lee,et al.  Signal Detection of Adverse Drug Reaction of Amoxicillin Using the Korea Adverse Event Reporting System Database , 2016, Journal of Korean medical science.

[16]  A. Bate,et al.  Quantitative signal detection using spontaneous ADR reporting , 2009, Pharmacoepidemiology and drug safety.

[17]  R. Meyboom,et al.  Pharmacovigilance in Perspective , 1999, Drug safety.

[18]  Ju-Young Shin,et al.  Bacillus Calmette-Guérin (BCG) vaccine safety surveillance in the Korea Adverse Event Reporting System using the tree-based scan statistic and conventional disproportionality-based algorithms. , 2020, Vaccine.

[19]  Hun-Sung Kim,et al.  Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records , 2017, J. Am. Medical Informatics Assoc..

[20]  Signal Detection of Adverse Drug Reaction of Zolpidem Using the Korea Adverse Event Reporting System Database , 2018 .

[21]  Danny H. Lee,et al.  Analysis of National Pharmacovigilance Data Associated with Statin Use in Korea , 2017, Basic & clinical pharmacology & toxicology.

[22]  D Yoon,et al.  Detection of Adverse Drug Reaction Signals Using an Electronic Health Records Database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) Algorithm , 2012, Clinical pharmacology and therapeutics.

[23]  Bengt Jönsson,et al.  Pharmacoeconomics of adverse drug reactions , 2004, Fundamental & clinical pharmacology.