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Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance

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Abstract

Introduction

Integration of controlled vocabulary-based electronic health record (EHR) observational data is essential for real-time large-scale pharmacovigilance studies.

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

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Acknowledgements

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.

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Author notes

  1. Suehyun Lee and Jongsoo Han contributed equally to this work.

Authors and Affiliations

  1. Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea

    Suehyun Lee, Jongsoo Han, Grace Juyun Kim, Kye Hwa Lee & Ju Han Kim

  2. Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Korea

    Suehyun Lee

  3. Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea

    Rae Woong Park

  4. Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea

    John Hoon Rim

  5. Physician-Scientist Program, Department of Medicine, Yonsei University Graduate School of Medicine, Seoul, Korea

    John Hoon Rim & Jooyoung Cho

  6. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea

    John Hoon Rim

  7. Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea

    Jooyoung Cho

  8. Precision Medicine Center, Seoul National University Hospital, Seoul, Korea

    Kye Hwa Lee & Ju Han Kim

  9. College of Nursing, Catholic University of Pusan, Busan, Korea

    Jisan Lee

  10. College of Nursing, Seattle University, Seattle, USA

    Sujeong Kim

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  1. Suehyun Lee
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Correspondence to Ju Han Kim.

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Funding

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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Lee, S., Han, J., Park, R. et al. Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance. Drug Saf 42, 657–670 (2019). https://doi.org/10.1007/s40264-018-0767-7

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