Health Care Data Science Center, Konyang University Hospital, 158 Gwanjeo-dong-ro, Seo-gu, Daejeon, Republic of Korea.
Departments of Biomedical Informatics, Konyang University College of Medicine, 158 Gwanjeo-dong-ro, Seo-gu, Daejeon, Republic of Korea.
BMC Med Inform Decis Mak. 2021 May 17;21(1):159. doi: 10.1186/s12911-021-01520-y.
Adverse drug reactions (ADRs) are regarded as a major cause of death and a major contributor to public health costs. For the active surveillance of drug safety, the use of real-world data and real-world evidence as part of the overall pharmacovigilance process is important. In this regard, many studies apply the data-driven approaches to support pharmacovigilance. We developed a pharmacovigilance data-processing pipeline (PDP) that utilized electronic health records (EHR) and spontaneous reporting system (SRS) data to explore pharmacovigilance signals.
To this end, we integrated two medical data sources: Konyang University Hospital (KYUH) EHR and the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). As part of the presented PDP, we converted EHR data on the Observation Medical Outcomes Partnership (OMOP) data model. To evaluate the ability of using the proposed PDP for pharmacovigilance purposes, we performed a statistical validation using drugs that induce ear disorders.
To validate the presented PDP, we extracted six drugs from the EHR that were significantly involved in ADRs causing ear disorders: nortriptyline, (hazard ratio [HR] 8.06, 95% CI 2.41-26.91); metoclopramide (HR 3.35, 95% CI 3.01-3.74); doxycycline (HR 1.73, 95% CI 1.14-2.62); digoxin (HR 1.60, 95% CI 1.08-2.38); acetaminophen (HR 1.59, 95% CI 1.47-1.72); and sucralfate (HR 1.21, 95% CI 1.06-1.38). In FAERS, the strongest associations were found for nortriptyline (reporting odds ratio [ROR] 1.94, 95% CI 1.73-2.16), sucralfate (ROR 1.22, 95% CI 1.01-1.45), doxycycline (ROR 1.30, 95% CI 1.20-1.40), and hydroxyzine (ROR 1.17, 95% CI 1.06-1.29). We confirmed the results in a meta-analysis using random and fixed models for doxycycline, hydroxyzine, metoclopramide, nortriptyline, and sucralfate.
The proposed PDP could support active surveillance and the strengthening of potential ADR signals via real-world data sources. In addition, the PDP was able to generate real-world evidence for drug safety.
药物不良反应（ADR）被认为是死亡的主要原因之一，也是公共卫生成本的主要贡献者。为了主动监测药物安全性，将真实世界的数据和真实世界的证据作为整体药物警戒过程的一部分是很重要的。在这方面，许多研究应用数据驱动的方法来支持药物警戒。我们开发了一个药物警戒数据处理管道（PDP），该管道利用电子健康记录（EHR）和自发报告系统（SRS）数据来探索药物警戒信号。
为此，我们整合了两个医疗数据源：光阳大学医院（KYUH）的 EHR 和美国食品和药物管理局（FDA）的不良事件报告系统（FAERS）。作为提出的 PDP 的一部分，我们将 EHR 数据转换为观察医疗成果伙伴关系（OMOP）数据模型。为了评估使用所提出的 PDP 进行药物警戒目的的能力，我们使用诱导耳部疾病的药物进行了统计验证。
为了验证所提出的 PDP，我们从 EHR 中提取了六种与导致耳部疾病的 ADR 显著相关的药物：去甲替林（危险比[HR]8.06，95%置信区间[CI]2.41-26.91）；甲氧氯普胺（HR 3.35，95%CI 3.01-3.74）；多西环素（HR 1.73，95%CI 1.14-2.62）；地高辛（HR 1.60，95%CI 1.08-2.38）；对乙酰氨基酚（HR 1.59，95%CI 1.47-1.72）；和硫糖铝（HR 1.21，95%CI 1.06-1.38）。在 FAERS 中，发现与去甲替林（报告比值比[ROR]1.94，95%CI 1.73-2.16）、硫糖铝（ROR 1.22，95%CI 1.01-1.45）、多西环素（ROR 1.30，95%CI 1.20-1.40）和羟嗪（ROR 1.17，95%CI 1.06-1.29）的相关性最强。我们使用随机和固定模型对多西环素、羟嗪、甲氧氯普胺、去甲替林和硫糖铝进行了荟萃分析，证实了这些结果。
所提出的 PDP 可以通过真实世界的数据来源支持主动监测和加强潜在的 ADR 信号。此外，该 PDP 能够为药物安全性提供真实世界的证据。
