| アブストラクト | The utility of big data in spontaneous adverse drug reactions (ADRs) reporting systems has improved the pharmacovigilance process. However, identifying culprit drugs in ADRs remains challenging, although it is one of the foremost steps to managing ADRs. Aiming to estimate the likelihood of prescribed drugs being culprit drugs for given ADRs, we devised a Bayesian estimation model based on the Japanese Adverse Drug Events Reports (JADER) database. After developing the model, a validation study was conducted with 67 ADR reports with a gross of 1,387 drugs (67 culprit drugs and 1,320 concomitant drugs) prescribed and recorded at Yamaguchi University Hospital. As a result, the model estimated a culprit drug of ADRs with acceptable accuracy (area under the receiver operating characteristic curve 0.93 [95% confidence interval 0.88-0.97]). The estimation results provided by the model to healthcare practitioners can be used as one clue to determine the culprit drugs for various ADRs, which will improve the management of ADRs by shortening the treatment turnaround time and increasing the precision of diagnosis, leading to minimizing the adverse effects on patients. |
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| ジャーナル名 | Clinical pharmacology and therapeutics |
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| 投稿日 | 2023/2/9 |
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| 投稿者 | Hamada, Kazuki; Abe, Takeshi; Kouda, Kyoji; Nakatsui, Masahiko; Kitahara, Takashi; Matsunaga, Kazuto; Asai, Yoshiyuki |
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| 組織名 | Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi;University.;Department of Respiratory Medicine and Infectious Disease, Graduate School of;Medicine, Yamaguchi University.;AI Systems Medicine Research and Training Center, Graduate School of Medicine,;Yamaguchi University and Yamaguchi University Hospital.;Department of Clinical Pharmacology, Graduate School of Medicine, Yamaguchi;University Hospital. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/36752635/ |
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