ジナンの有害事象報告システムデータベースに基づく神経保護剤の安全性警報信号の採掘と評価: 遡及的研究

Yu-Yao Guan¹, Jing Yang¹, Ying-Mei Qi²

¹Department of Pharmacy, Shandong Provincial Third Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.

Frontiers in pharmacology

|August 21, 2025

まとめ

データマイニングは神経保護剤の安全性信号を特定し,人口統計と薬物使用における有害事象の変動を明らかにした. この分析は,これらの薬に関連する臨床的リスクを減らすのに役立ちます.

科学分野:

薬用警戒について
薬物安全性
神経薬理学

背景:

神経保護剤は調査中ですが,規制上のギャップと様々な証拠が安全性に関する懸念を提起しています.
販売後の監視は,これらの薬の実際の安全性プロフィールを理解するために不可欠です.

研究の目的:

ニューロプロテクティブ剤の安全性に関する警告信号をデータマイニングで調査する.
神経保護剤の安全な臨床使用のための基礎を確立する.

主な方法:

ジナンの有害事象 (AE) 報告システム (2000年1月~2022年3月) を利用した.
分析された患者データ,報告,およびサブグループおよび感受性分析を含む一般的なAE.
4つの不釣り合いの分析方法 (PRR,ROR,BCPNN,MHRA) を適用し,安全性信号を検出し,中国,米国,日本の医薬品ラベルと比較した.

主要な成果:

16万8,314件の有害事象のうち,2,094件の有害事象が神経保護剤と関連していた.
年齢,性別,および併用薬の使用によってリスクシグナルの有意な変動が観察されました.
多数の安全性信号が検出され,中国と日本の薬剤のラベルに記載されていない多くの有害事象が検出されました.

結論:

Jinan AE報告システムは,販売後の神経保護剤の警告信号を効果的にマイニングします.
このデータマイニングアプローチは,神経保護剤の臨床適用に関連するリスクを減らすのに役立ちます.

キーワード:

有害事象データマイニング神経保護剤安全警報信号ストローク

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