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Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety

INTRODUCTION: Signal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union. OBJECTIVES: We aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networ...

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Autores principales: Pétervári, Mátyás, Benczik, Bettina, Balogh, Olivér M., Petrovich, Balázs, Ágg, Bence, Ferdinandy, Péter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561003/
https://www.ncbi.nlm.nih.gov/pubmed/36198930
http://dx.doi.org/10.1007/s40264-022-01225-9
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author Pétervári, Mátyás
Benczik, Bettina
Balogh, Olivér M.
Petrovich, Balázs
Ágg, Bence
Ferdinandy, Péter
author_facet Pétervári, Mátyás
Benczik, Bettina
Balogh, Olivér M.
Petrovich, Balázs
Ágg, Bence
Ferdinandy, Péter
author_sort Pétervári, Mátyás
collection PubMed
description INTRODUCTION: Signal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union. OBJECTIVES: We aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networks. METHODS: ICSRs of five cardiovascular adverse events were requested from EudraVigilance. We developed Vigilace™, a web-based application to build network representation of ICSRs. Three network-based signal scores, which we termed NEWS (normalized edge weight for signals) scores, were calculated by normalizing the weight of each edge in the report-based weighted network by the weight of the same edge in topological weighted networks. Depending on the third node in topological network edges, we defined full-, adverse event-, and drug-type NEWS scores. Area under the receiver operating characteristic curves (AUROC) were analyzed to compare the reporting odds ratio (ROR) and NEWS scores. RESULTS: Overall, 72,475 ICSRs were accessed from EudraVigilance. Drug-type NEWS (NEWS(D)) score performed better (DeLong test, p-value <0.05) compared with the ROR in case of four adverse events: acute myocardial infarction (AUROC: 0.856 vs. 0.720), arrhythmia (0.657 vs. 0.614), pulmonary hypertension (0.861 vs. 0.720), and QT prolongation (0.830 vs. 0.749). Postural orthostatic tachycardia syndrome was excluded due to the lack of reference data. CONCLUSION: This is the first demonstration that report-based weighting normalized by topological weighting of co-reported drugs, which we termed as NEWS(D) score, can perform better compared with the ROR. An application was developed for ICSR network analysis that facilitates the calculation of this score. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-022-01225-9.
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spelling pubmed-95610032022-10-15 Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety Pétervári, Mátyás Benczik, Bettina Balogh, Olivér M. Petrovich, Balázs Ágg, Bence Ferdinandy, Péter Drug Saf Original Research Article INTRODUCTION: Signal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union. OBJECTIVES: We aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networks. METHODS: ICSRs of five cardiovascular adverse events were requested from EudraVigilance. We developed Vigilace™, a web-based application to build network representation of ICSRs. Three network-based signal scores, which we termed NEWS (normalized edge weight for signals) scores, were calculated by normalizing the weight of each edge in the report-based weighted network by the weight of the same edge in topological weighted networks. Depending on the third node in topological network edges, we defined full-, adverse event-, and drug-type NEWS scores. Area under the receiver operating characteristic curves (AUROC) were analyzed to compare the reporting odds ratio (ROR) and NEWS scores. RESULTS: Overall, 72,475 ICSRs were accessed from EudraVigilance. Drug-type NEWS (NEWS(D)) score performed better (DeLong test, p-value <0.05) compared with the ROR in case of four adverse events: acute myocardial infarction (AUROC: 0.856 vs. 0.720), arrhythmia (0.657 vs. 0.614), pulmonary hypertension (0.861 vs. 0.720), and QT prolongation (0.830 vs. 0.749). Postural orthostatic tachycardia syndrome was excluded due to the lack of reference data. CONCLUSION: This is the first demonstration that report-based weighting normalized by topological weighting of co-reported drugs, which we termed as NEWS(D) score, can perform better compared with the ROR. An application was developed for ICSR network analysis that facilitates the calculation of this score. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-022-01225-9. Springer International Publishing 2022-10-06 2022 /pmc/articles/PMC9561003/ /pubmed/36198930 http://dx.doi.org/10.1007/s40264-022-01225-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research Article
Pétervári, Mátyás
Benczik, Bettina
Balogh, Olivér M.
Petrovich, Balázs
Ágg, Bence
Ferdinandy, Péter
Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title_full Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title_fullStr Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title_full_unstemmed Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title_short Network Analysis for Signal Detection in Spontaneous Adverse Event Reporting Database: Application of Network Weighting Normalization to Characterize Cardiovascular Drug Safety
title_sort network analysis for signal detection in spontaneous adverse event reporting database: application of network weighting normalization to characterize cardiovascular drug safety
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561003/
https://www.ncbi.nlm.nih.gov/pubmed/36198930
http://dx.doi.org/10.1007/s40264-022-01225-9
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