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A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system

BACKGROUND: Patient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have...

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Autores principales: Noguchi, Yoshihiro, Ueno, Anri, Otsubo, Manami, Katsuno, Hayato, Sugita, Ikuto, Kanematsu, Yuta, Yoshida, Aki, Esaki, Hiroki, Tachi, Tomoya, Teramachi, Hitomi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887208/
https://www.ncbi.nlm.nih.gov/pubmed/29621976
http://dx.doi.org/10.1186/s12859-018-2137-y
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author Noguchi, Yoshihiro
Ueno, Anri
Otsubo, Manami
Katsuno, Hayato
Sugita, Ikuto
Kanematsu, Yuta
Yoshida, Aki
Esaki, Hiroki
Tachi, Tomoya
Teramachi, Hitomi
author_facet Noguchi, Yoshihiro
Ueno, Anri
Otsubo, Manami
Katsuno, Hayato
Sugita, Ikuto
Kanematsu, Yuta
Yoshida, Aki
Esaki, Hiroki
Tachi, Tomoya
Teramachi, Hitomi
author_sort Noguchi, Yoshihiro
collection PubMed
description BACKGROUND: Patient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods. METHODS: In this study, in order to establish a simple method to explore ADEs in patients with kidney or liver injury as a background disease, the AR and proportional reporting ratio (PRR) signal detection methods were compared. We used oral medicine SRS data from the Japanese Adverse Drug Event Report database (JADER), and used AR as the proposed search method and PRR as the conventional method for comparison. “Rule count ≥ 3”, “min lift value > 1”, and “min conviction value > 1” were used as the AR detection criteria, and the PRR detection criteria were “Rule count ≥3”, “PRR ≥ 2”, and “χ(2) ≥ 4”. RESULTS: In patients with kidney injury, the AR method had a sensitivity of 99.58%, specificity of 94.99%, and Youden’s index of 0.946, while in patients with liver injury, the sensitivity, specificity, and Youden’s index were 99.57%, 94.87%, and 0.944, respectively. Additionally, the lift value and the strength of the signal were positively correlated. CONCLUSIONS: It was suggested that computation using AR might be simple with the detection power equivalent to that of the conventional signal detection method as PRR. In addition, AR can theoretically be applicable to SRS other than JADER. Therefore, complicated conditions (patient’s background etc.) that must take factors other than the ADE into consideration can be easily explored by selecting the AR as the first screening for ADE exploration in pharmacovigilance using SRS.
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spelling pubmed-58872082018-04-09 A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system Noguchi, Yoshihiro Ueno, Anri Otsubo, Manami Katsuno, Hayato Sugita, Ikuto Kanematsu, Yuta Yoshida, Aki Esaki, Hiroki Tachi, Tomoya Teramachi, Hitomi BMC Bioinformatics Research Article BACKGROUND: Patient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods. METHODS: In this study, in order to establish a simple method to explore ADEs in patients with kidney or liver injury as a background disease, the AR and proportional reporting ratio (PRR) signal detection methods were compared. We used oral medicine SRS data from the Japanese Adverse Drug Event Report database (JADER), and used AR as the proposed search method and PRR as the conventional method for comparison. “Rule count ≥ 3”, “min lift value > 1”, and “min conviction value > 1” were used as the AR detection criteria, and the PRR detection criteria were “Rule count ≥3”, “PRR ≥ 2”, and “χ(2) ≥ 4”. RESULTS: In patients with kidney injury, the AR method had a sensitivity of 99.58%, specificity of 94.99%, and Youden’s index of 0.946, while in patients with liver injury, the sensitivity, specificity, and Youden’s index were 99.57%, 94.87%, and 0.944, respectively. Additionally, the lift value and the strength of the signal were positively correlated. CONCLUSIONS: It was suggested that computation using AR might be simple with the detection power equivalent to that of the conventional signal detection method as PRR. In addition, AR can theoretically be applicable to SRS other than JADER. Therefore, complicated conditions (patient’s background etc.) that must take factors other than the ADE into consideration can be easily explored by selecting the AR as the first screening for ADE exploration in pharmacovigilance using SRS. BioMed Central 2018-04-05 /pmc/articles/PMC5887208/ /pubmed/29621976 http://dx.doi.org/10.1186/s12859-018-2137-y Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Noguchi, Yoshihiro
Ueno, Anri
Otsubo, Manami
Katsuno, Hayato
Sugita, Ikuto
Kanematsu, Yuta
Yoshida, Aki
Esaki, Hiroki
Tachi, Tomoya
Teramachi, Hitomi
A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title_full A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title_fullStr A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title_full_unstemmed A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title_short A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
title_sort simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5887208/
https://www.ncbi.nlm.nih.gov/pubmed/29621976
http://dx.doi.org/10.1186/s12859-018-2137-y
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