Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study
Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/60054 |
| Acceso en línea: | http://hdl.handle.net/10230/60054 http://dx.doi.org/10.1007/s40264-023-01353-w |
| Access Level: | acceso abierto |
| Palabra clave: | Farmacovigilància Farmacoepidemiologia |
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Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| title |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| spellingShingle |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study Gauffin, Oskar Farmacovigilància Farmacoepidemiologia |
| title_short |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| title_full |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| title_fullStr |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| title_full_unstemmed |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| title_sort |
Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network study |
| dc.creator.none.fl_str_mv |
Gauffin, Oskar Mayer, Miguel Ángel, 1960- Ramírez Anguita, Juan Manuel Norén, Niklas |
| author |
Gauffin, Oskar |
| author_facet |
Gauffin, Oskar Mayer, Miguel Ángel, 1960- Ramírez Anguita, Juan Manuel Norén, Niklas |
| author_role |
author |
| author2 |
Mayer, Miguel Ángel, 1960- Ramírez Anguita, Juan Manuel Norén, Niklas |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Farmacovigilància Farmacoepidemiologia |
| topic |
Farmacovigilància Farmacoepidemiologia |
| description |
Introduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research. |
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2023 |
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2023 2024 2024 |
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info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
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http://hdl.handle.net/10230/60054 http://dx.doi.org/10.1007/s40264-023-01353-w |
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http://hdl.handle.net/10230/60054 http://dx.doi.org/10.1007/s40264-023-01353-w |
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Inglés |
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Inglés |
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Drug Saf. 2023 Dec;46(12):1335-52 |
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http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc/4.0/ |
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openAccess |
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Springer |
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Springer |
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reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Supporting pharmacovigilance signal validation and prioritization with analyses of routinely collected health data: Lessons learned from an EHDEN network studyGauffin, OskarMayer, Miguel Ángel, 1960-Ramírez Anguita, Juan ManuelNorén, NiklasFarmacovigilànciaFarmacoepidemiologiaIntroduction: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. Objective: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Methods: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Results: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Conclusions: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.Springer202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/60054http://dx.doi.org/10.1007/s40264-023-01353-wreponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésDrug Saf. 2023 Dec;46(12):1335-52© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial 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-nc/4.0/.http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/600542026-05-29T05:05:01Z |
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