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Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea

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Abstract

Introduction

With the availability of retrospective pharmacovigilance data, the common data model (CDM) has been identified as an efficient approach towards anonymized multicenter analysis; however, the establishment of a suitable model for individual medical systems and applications supporting their analysis is a challenge.

Objective

The aim of this study was to construct a specialized Korean CDM (K-CDM) for pharmacovigilance systems based on a clinical scenario to detect adverse drug reactions (ADRs).

Methods

De-identified patient records (n = 5,402,129) from 13 institutions were converted to the K-CDM. From 2005 to 2017, 37,698,535 visits, 39,910,849 conditions, 259,594,727 drug exposures, and 30,176,929 procedures were recorded. The K-CDM, which comprises three layers, is compatible with existing models and is potentially adaptable to extended clinical research. Local codes for electronic medical records (EMRs), including diagnosis, drug prescriptions, and procedures, were mapped using standard vocabulary. Distributed queries based on clinical scenarios were developed and applied to K-CDM through decentralized or distributed networks.

Results

Meta-analysis of drug relative risk ratios from ten institutions revealed that non-steroidal anti-inflammatory drugs (NSAIDs) increased the risk of gastrointestinal hemorrhage by twofold compared with aspirin, and non-vitamin K anticoagulants decreased cerebrovascular bleeding risk by 0.18-fold compared with warfarin.

Conclusion

These results are similar to those from previous studies and are conducive for new research, thereby demonstrating the feasibility of K-CDM for pharmacovigilance. However, the low quality of original EMR data, incomplete mapping, and heterogeneity between institutions reduced the validity of the analysis, thus necessitating continuous calibration among researchers, clinicians, and the government.

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Acknowledgements

The authors thank Bonggi Kim, PhD (Office of Pharmacoepidemiology and Bigdata Analytics, Department of Drug Safety Information, Korea Institute of Drug Safety and Risk Management) for his contribution to the operation and management of the MOA Network.

Author information

Author notes

  1. Seon Choe and Suhyun Lee contributed equally to this work.

Authors and Affiliations

  1. Division of Biomedical Informatics, Systems Biomedical Informatics Research Centre, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Seon Choe, Chan Hee Park, Jeong Hoon Lee & Ju Han Kim

  2. Department of Computer Engineering, Gachon University, Seongnam, Republic of Korea

    Dong Yoon Kang

  3. Center for Research Resource Standardization, Research Institution for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea

    Hyo Jung Kim

  4. Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea

    Hyo Jung Kim

  5. Department of Pathology, Hallym University College of Medicine, Chuncheon, Republic of Korea

    Sun-ju Byeon

  6. Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Republic of Korea

    Jeong-Hee Choi

  7. Department of Pediatrics, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea

    Hyeon-Jong Yang

  8. Department of Allergy and Clinical Immunology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Republic of Korea

    Da Woon Sim

  9. Department of Ophthalmology, Hallym University College of Medicine, Chuncheon, Republic of Korea

    Bum-Joo Cho

  10. Department of Internal Medicine, Seoul Paik Hospital, Inje University, Seoul, Republic of Korea

    Hoseok Koo

  11. Department of Internal Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea

    Min-Gyu Kang

  12. Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea

    Ji Bong Jeong

  13. Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    In Young Choi

  14. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

    Sae-Hoon Kim

  15. Department of Internal Medicine, Kangwon National University College of Medicine, Chuncheon, Republic of Korea

    Woo Jin Kim

  16. Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Republic of Korea

    Jae-Woo Jung

  17. Department of Preventive Medicine, Naeun Hospital, Incheon, Republic of Korea

    Sang-Hoon Lhee

  18. CM General Hospital, Seoul, Republic of Korea

    Young-Jin Ko

  19. Department of Internal Medicine, Pusan National University College of Medicine, Busan, Republic of Korea

    Hye-Kyung Park

  20. Department of Preventive Medicine, Ulsan University Hospital, 877, Bangeojinsunhwando-ro, Dong-gu, Ulsan, 44033, Republic of Korea

    Suhyun Lee

Authors
  1. Seon Choe
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  2. Suhyun Lee
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  3. Chan Hee Park
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  4. Jeong Hoon Lee
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  6. Sun-ju Byeon
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  20. Hye-Kyung Park
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  21. Dong Yoon Kang
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  22. Ju Han Kim
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Corresponding authors

Correspondence to Dong Yoon Kang or Ju Han Kim.

Ethics declarations

Funding

This study was supported by a research grant provided as part of the 2018 MFDS project titled ‘Evaluation and Reduction Study of NSAIDS Side Effects in Patients with Chronic Pain and Arthritis’ (18172 Drug Safety 149-1) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI19C1310).

Conflicts of interest

Seon Choe, Suhyun Lee, Chan Hee Park, Jeong Hoon Lee, Hyo Jung Kim, Sun-ju Byeon, Jeong-Hee Choi, Hyeon-Jong Yang, Da Woon Sim, Bum-Joo Cho, Hoseok koo, Min-Gyu Kang, Ji Bong Jeong, In Young Choi, Sae-Hoon Kim, Woo Jin Kim, Jae-Woo Jung, Sang-Hoon Lhee, Young-Jin Ko, Hye-Kyung Park, Dong Yoon Kang, and Ju Han Kim declare that they have no competing interests.

Ethics approval

Ethical approval for this study was granted by the Institutional Review Board of the Seoul National University (H-1707-135-871).

Consent to participate and consent for publication

Consent from participants was not required as this study extracted anonymous data from an electronic health database under Korean regulations and approval from the Seoul National University.

Availability of data and material

All data analyzed in this study are included in this published article.

Code availability (software application or custom code)

The code generated during the current study is available from the corresponding author upon reasonable request.

Author contributions

JHK designed and supervised the study, and SHL designed the K-CDM architecture and managed the mapping. DYK developed and validated the clinical scenario, and HJK constructed the query applicable to each institution. CHP and JHL proceeded with the K-CDM ETL, and SC analyzed the query results and prepared the manuscript. The remaining authors, who oversee each participating organization, extracted the EMRs for conversion to the CDM and performed queries to obtain the results. All authors read and approved the final version of the manuscript.

Supplementary Information

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Supplementary file 1 (PDF 397 KB)

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Choe, S., Lee, S., Park, C.H. et al. Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea. Drug Saf 46, 647–660 (2023). https://doi.org/10.1007/s40264-023-01296-2

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