Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance
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Integration of controlled vocabulary-based electronic health record (EHR) observational data is essential for real-time large-scale pharmacovigilance studies.
To provide a semantically enriched adverse drug reaction (ADR) dictionary for post-market drug safety research and enable multicenter EHR-based extensive ADR signal detection and evaluation, we developed a comprehensive controlled vocabulary-based ADR signal dictionary (CVAD) for pharmacovigilance.
A CVAD consists of (1) administrative disease classifications of the International Classification of Diseases (ICD) codes mapped to the Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA® PTs); (2) two teaching hospitals’ codes for laboratory test results mapped to the Logical Observation Identifiers Names and Codes (LOINC) terms and MedDRA® PTs; and (3) clinical narratives and ADRs encoded by standard nursing statements (encoded by the International Classification for Nursing Practice [ICNP]) mapped to the World Health Organization–Adverse Reaction Terminology (WHO-ART) terms and MedDRA® PTs.
Of the standard 4514 MedDRA® PTs from Side Effect Resources (SIDER) 4.1, 1130 (25.03%), 942 (20.86%), and 83 (1.83%) terms were systematically mapped to clinical narratives, laboratory test results, and disease classifications, respectively. For the evaluation, we loaded multi-source EHR data. We first performed a clinical expert review of the CVAD clinical relevance and a three-drug ADR case analyses consisting of linezolid-induced thrombocytopenia, warfarin-induced bleeding tendency, and vancomycin-induced acute kidney injury.
CVAD had a high coverage of ADRs and integrated standard controlled vocabularies to the EHR data sources, and researchers can take advantage of these features for EHR observational data-based extensive pharmacovigilance studies to improve sensitivity and specificity.
The authors would like to acknowledge the National Research Foundation of Korea (NRF) and Korean Health Technology R&D Project, Ministry of Health and Welfare. The authors thank the anonymous reviewers for their helpful feedback.
Compliance with Ethical Standards
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science ICT and Future Planning (MSIP) (2018R1D1A1B07049155) and by a grant from the Korean Health Technology R&D Project, Ministry of Health and Welfare (HI13C2164, HI16C11280000). This research was supported by a grant (16183MFDS541) from Ministry of Food and Drug Safety in 2018.
Conflict of interest
Ju Han Kim, Suehyun Lee, Jongsoo Han, Rae Woong Park, Grace Juyun Kim, John Hoon Rim, Jooyoung Cho, Kye Hwa Lee, Jisan Lee, and Sujeong Kim have no conflicts of interest directly relevant to the content of this study. The results of this study do not reflect the views of the National Research Foundation of Korea (NRF) or Ministry of Health and Welfare.
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