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

Seon Choe, Suehyun 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 ()
Additional contact information
Seon Choe: Seoul National University College of Medicine
Suehyun Lee: Gachon University
Chan Hee Park: Seoul National University College of Medicine
Jeong Hoon Lee: Seoul National University College of Medicine
Hyo Jung Kim: Samsung Medical Center
Sun-ju Byeon: Hallym University College of Medicine
Jeong-Hee Choi: Hallym University Dongtan Sacred Heart Hospital
Hyeon-Jong Yang: Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine
Da Woon Sim: Chonnam National University Medical School
Bum-Joo Cho: Hallym University College of Medicine
Hoseok Koo: Inje University
Min-Gyu Kang: Chungbuk National University Hospital
Ji Bong Jeong: Seoul National University Boramae Medical Center
In Young Choi: The Catholic University of Korea
Sae-Hoon Kim: Seoul National University Bundang Hospital
Woo Jin Kim: Kangwon National University College of Medicine
Jae-Woo Jung: Chung-Ang University College of Medicine
Sang-Hoon Lhee: Naeun Hospital
Young-Jin Ko: CM General Hospital
Hye-Kyung Park: Pusan National University College of Medicine
Dong Yoon Kang: Ulsan University Hospital
Ju Han Kim: Seoul National University College of Medicine

Drug Safety, 2023, vol. 46, issue 7, No 4, 647-660

Abstract: 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.

Date: 2023
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DOI: 10.1007/s40264-023-01296-2

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