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Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions

Background: Adverse drug reaction (ADR) signal mining is essential for assessing drug safety. However, the currently available methods for this are rather cumbersome. Objective: We aimed to develop a drug risk analysis and assessment system using Java language and conduct pharmacovigilance data mini...

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Autores principales: Guan, Yuyao, Ji, Lei, Zheng, Lei, Yang, Jing, Qin, Yizhuo, Ding, Ning, Miao, Ting, Liu, Xuemei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577403/
https://www.ncbi.nlm.nih.gov/pubmed/36267271
http://dx.doi.org/10.3389/fphar.2022.892503
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author Guan, Yuyao
Ji, Lei
Zheng, Lei
Yang, Jing
Qin, Yizhuo
Ding, Ning
Miao, Ting
Liu, Xuemei
author_facet Guan, Yuyao
Ji, Lei
Zheng, Lei
Yang, Jing
Qin, Yizhuo
Ding, Ning
Miao, Ting
Liu, Xuemei
author_sort Guan, Yuyao
collection PubMed
description Background: Adverse drug reaction (ADR) signal mining is essential for assessing drug safety. However, the currently available methods for this are rather cumbersome. Objective: We aimed to develop a drug risk analysis and assessment system using Java language and conduct pharmacovigilance data mining for fluoroquinolones at our hospital. Methods: We used ADR data reported by Shandong Provincial Third Hospital between July 2007 and August 2021. The signal detection methods included proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), Medicines and Healthcare products Regulatory Agency (MHRA). The BCPNN method was used as the reference standard for comparing the remaining three signal detection methods based on sensitivity, specificity, positive predictive value, negative predictive value, and Jorden index. Results: The hospital database contained a total of 2,621 ADR reports, among which 263 were attributed to fluoroquinolones. There were 391 fluoroquinolone-ADR pairs. Using the PRR, ROR, MHRA, and BCPNN method, we detected 13 signals, 13 signals, 10 signals, and 11 weak signals, respectively. After signal detection, levofloxacin and moxifloxacin were shown to induce high risk signals for mental and sleep disorders, with the signal intensity of moxifloxacin being the most significant. Compared with BCPNN, the PRR and ROR methods showed better sensitivity, whereas the MHRA method showed better specificity. Conclusion: We developed a drug risk analysis and assessment system that can help hospitals and other medical institutions to detect and analyse ADR signals in the self-reporting system database, and thus improve drug safety. Further, it indicates that the central nervous system damage caused by fluoroquinolones should be monitored closely, and thus provides a reference for the clinical application of these drugs.
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spelling pubmed-95774032022-10-19 Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions Guan, Yuyao Ji, Lei Zheng, Lei Yang, Jing Qin, Yizhuo Ding, Ning Miao, Ting Liu, Xuemei Front Pharmacol Pharmacology Background: Adverse drug reaction (ADR) signal mining is essential for assessing drug safety. However, the currently available methods for this are rather cumbersome. Objective: We aimed to develop a drug risk analysis and assessment system using Java language and conduct pharmacovigilance data mining for fluoroquinolones at our hospital. Methods: We used ADR data reported by Shandong Provincial Third Hospital between July 2007 and August 2021. The signal detection methods included proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), Medicines and Healthcare products Regulatory Agency (MHRA). The BCPNN method was used as the reference standard for comparing the remaining three signal detection methods based on sensitivity, specificity, positive predictive value, negative predictive value, and Jorden index. Results: The hospital database contained a total of 2,621 ADR reports, among which 263 were attributed to fluoroquinolones. There were 391 fluoroquinolone-ADR pairs. Using the PRR, ROR, MHRA, and BCPNN method, we detected 13 signals, 13 signals, 10 signals, and 11 weak signals, respectively. After signal detection, levofloxacin and moxifloxacin were shown to induce high risk signals for mental and sleep disorders, with the signal intensity of moxifloxacin being the most significant. Compared with BCPNN, the PRR and ROR methods showed better sensitivity, whereas the MHRA method showed better specificity. Conclusion: We developed a drug risk analysis and assessment system that can help hospitals and other medical institutions to detect and analyse ADR signals in the self-reporting system database, and thus improve drug safety. Further, it indicates that the central nervous system damage caused by fluoroquinolones should be monitored closely, and thus provides a reference for the clinical application of these drugs. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577403/ /pubmed/36267271 http://dx.doi.org/10.3389/fphar.2022.892503 Text en Copyright © 2022 Guan, Ji, Zheng, Yang, Qin, Ding, Miao and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Guan, Yuyao
Ji, Lei
Zheng, Lei
Yang, Jing
Qin, Yizhuo
Ding, Ning
Miao, Ting
Liu, Xuemei
Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title_full Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title_fullStr Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title_full_unstemmed Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title_short Development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
title_sort development of a drug risk analysis and assessment system and its application in signal excavation and analysis of 263 cases of fluoroquinolone-induced adverse reactions
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577403/
https://www.ncbi.nlm.nih.gov/pubmed/36267271
http://dx.doi.org/10.3389/fphar.2022.892503
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