Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea.
Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Korea.
Drug Saf. 2019 May;42(5):657-670. doi: 10.1007/s40264-018-0767-7.
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.
将基于受控词汇的电子健康记录（EHR）观测数据进行整合对于实时的大规模药物警戒研究至关重要。
为了进行上市后药物安全性研究中的语义丰富的药物不良反应（ADR）字典，并实现基于多中心 EHR 的广泛 ADR 信号检测和评估，我们开发了一个全面的基于受控词汇的药物警戒 ADR 信号字典（CVAD）。
CVAD 由以下部分组成：（1）国际疾病分类（ICD）代码的管理疾病分类映射到监管活动医学词典首选术语（MedDRA PTs）；（2）两所教学医院的实验室检验结果代码映射到逻辑观察标识符名称和代码（LOINC）术语和 MedDRA PTs；（3）临床叙述和由标准护理语句编码的 ADRs（由国际护理实践分类 [ICNP] 编码）映射到世界卫生组织不良反应术语（WHO-ART）术语和 MedDRA PTs。
在 SIDER 4.1 中的标准 4514 个 MedDRA PTs 中，分别有 1130（25.03%）、942（20.86%）和 83（1.83%）个术语系统地映射到临床叙述、实验室检验结果和疾病分类。在评估中，我们加载了多源 EHR 数据。我们首先对 CVAD 的临床相关性进行了临床专家审查，并进行了三药 ADR 案例分析，包括利奈唑胺引起的血小板减少症、华法林引起的出血倾向和万古霉素引起的急性肾损伤。
CVAD 涵盖了广泛的 ADR 并将标准受控词汇整合到 EHR 数据源中，研究人员可以利用这些功能进行基于 EHR 观测数据的广泛药物警戒研究，以提高敏感性和特异性。
