Department of Biomedical Informatics, College of Medicine, Konyang University, Daejeon, Republic of Korea.
Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea.
J Med Internet Res. 2022 Oct 6;24(10):e35464. doi: 10.2196/35464.
Pharmacovigilance using real-world data (RWD), such as multicenter electronic health records (EHRs), yields massively parallel adverse drug reaction (ADR) signals. However, proper validation of computationally detected ADR signals is not possible due to the lack of a reference standard for positive and negative associations.
This study aimed to develop a reference standard for ADR (RS-ADR) to streamline the systematic detection, assessment, and understanding of almost all drug-ADR associations suggested by RWD analyses.
We integrated well-known reference sets for drug-ADR pairs, including Side Effect Resource, Observational Medical Outcomes Partnership, and EU-ADR. We created a pharmacovigilance dictionary using controlled vocabularies and systematically annotated EHR data. Drug-ADR associations computed from MetaLAB and MetaNurse analyses of multicenter EHRs and extracted from the Food and Drug Administration Adverse Event Reporting System were integrated as "empirically determined" positive and negative reference sets by means of cross-validation between institutions.
The RS-ADR consisted of 1344 drugs, 4485 ADRs, and 6,027,840 drug-ADR pairs with positive and negative consensus votes as pharmacovigilance reference sets. After the curation of the initial version of RS-ADR, novel ADR signals such as "famotidine-hepatic function abnormal" were detected and reasonably validated by RS-ADR. Although the validation of the entire reference standard is challenging, especially with this initial version, the reference standard will improve as more RWD participate in the consensus voting with advanced pharmacovigilance dictionaries and analytic algorithms. One can check if a drug-ADR pair has been reported by our web-based search interface for RS-ADRs.
RS-ADRs enriched with the pharmacovigilance dictionary, ADR knowledge, and real-world evidence from EHRs may streamline the systematic detection, evaluation, and causality assessment of computationally detected ADR signals.
使用真实世界数据（RWD）进行药物警戒，例如多中心电子健康记录（EHR），可产生大量平行的药物不良反应（ADR）信号。然而，由于缺乏阳性和阴性关联的参考标准，无法对计算检测到的 ADR 信号进行适当验证。
本研究旨在开发一种药物不良反应参考标准（RS-ADR），以简化对 RWD 分析提示的几乎所有药物-ADR 关联的系统检测、评估和理解。
我们整合了著名的药物-ADR 对参考集，包括不良反应资源、观察性医疗结局伙伴关系和欧盟-ADR。我们使用控制词汇创建了药物警戒字典，并对 EHR 数据进行了系统注释。从多中心 EHR 的 MetaLAB 和 MetaNurse 分析中计算出的药物-ADR 关联，并从美国食品和药物管理局不良事件报告系统中提取出来，通过机构间的交叉验证作为“经验确定”的阳性和阴性参考集进行整合。
RS-ADR 由 1344 种药物、4485 种 ADR 和 6027840 对具有阳性和阴性共识投票的药物-ADR 对组成，作为药物警戒参考集。在 RS-ADR 的初始版本进行审核后，新的 ADR 信号，如“法莫替丁-肝功能异常”，通过 RS-ADR 被合理地检测和验证。尽管整个参考标准的验证具有挑战性，特别是对于这个初始版本，但是随着更多的 RWD 通过使用先进的药物警戒字典和分析算法参与共识投票，参考标准将会得到改善。人们可以通过我们的基于网络的 RS-ADR 搜索界面检查药物-ADR 对是否已经报告。
通过药物警戒字典、ADR 知识和 EHR 中的真实世界证据丰富的 RS-ADR，可能会简化计算检测到的 ADR 信号的系统检测、评估和因果关系评估。
