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An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources

BACKGROUND: 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 regar...

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Autores principales: Shin, Hyunah, Lee, Suehyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130307/
https://www.ncbi.nlm.nih.gov/pubmed/34001114
http://dx.doi.org/10.1186/s12911-021-01520-y
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author Shin, Hyunah
Lee, Suehyun
author_facet Shin, Hyunah
Lee, Suehyun
author_sort Shin, Hyunah
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01520-y.
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spelling pubmed-81303072021-05-18 An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources Shin, Hyunah Lee, Suehyun BMC Med Inform Decis Mak Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01520-y. BioMed Central 2021-05-17 /pmc/articles/PMC8130307/ /pubmed/34001114 http://dx.doi.org/10.1186/s12911-021-01520-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Shin, Hyunah
Lee, Suehyun
An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title_full An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title_fullStr An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title_full_unstemmed An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title_short An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources
title_sort omop-cdm based pharmacovigilance data-processing pipeline (pdp) providing active surveillance for adr signal detection from real-world data sources
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130307/
https://www.ncbi.nlm.nih.gov/pubmed/34001114
http://dx.doi.org/10.1186/s12911-021-01520-y
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