SynthesisBMC medical informatics and decision making2021
An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources.
Synthesis in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- Pharmacovigilance - Technological Advancements, Recent Developments and Innovations.Current drug safety · 2025Review
- Incorporating patient-reported outcome measures into routine paediatric pharmacovigilance: opportunities for safety monitoring with practical use scenarios from paediatric oncology.Frontiers in pharmacology · 2025Review
- Detection of novel drug-adverse drug reaction signals in rheumatoid arthritis and ankylosing spondylitis: analysis of Korean real-world biologics registry data.Scientific reports · 2024Article
- Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review.Frontiers in drug safety and regulation · 2024Review
- Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review.Drug safety · 2023Article
- Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdverse 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.
methodsTo 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.
resultsTo 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.
conclusionsThe 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.
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