Suehyun Lee1,2, Jongsoo Han1, Rae Woong Park3, Grace Juyun Kim1, John Hoon Rim4,5,6, Jooyoung Cho5,7, Kye Hwa Lee1,8, Jisan Lee9, Sujeong Kim10, Ju Han Kim11,12. 1. Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea. 2. Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Korea. 3. Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea. 4. Department of Laboratory Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. 5. Physician-Scientist Program, Department of Medicine, Yonsei University Graduate School of Medicine, Seoul, Korea. 6. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Korea. 7. Department of Laboratory Medicine, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea. 8. Precision Medicine Center, Seoul National University Hospital, Seoul, Korea. 9. College of Nursing, Catholic University of Pusan, Busan, Korea. 10. College of Nursing, Seattle University, Seattle, USA. 11. Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea. juhan@snu.ac.kr. 12. Precision Medicine Center, Seoul National University Hospital, Seoul, Korea. juhan@snu.ac.kr.
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.
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.
Authors: Robert Eriksson; Peter Bjødstrup Jensen; Sune Frankild; Lars Juhl Jensen; Søren Brunak Journal: J Am Med Inform Assoc Date: 2013-05-23 Impact factor: 4.497
Authors: Suehyun Lee; Jeong Hoon Lee; Grace Juyun Kim; Jong-Yeup Kim; Hyunah Shin; Inseok Ko; Seon Choe; Ju Han Kim Journal: J Med Internet Res Date: 2022-10-06 Impact factor: 7.076