首页|基于FAERS的培唑帕尼不良事件信号挖掘

基于FAERS的培唑帕尼不良事件信号挖掘

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
目的 为临床合理使用培唑帕尼提供参考。方法 通过美国食品和药物管理局不良事件报告系统(FAERS)获取 2009年 1月 1日至 2023 年 4 月 30 日以培唑帕尼为首要怀疑药物的ADE报告，利用报告比值比(ROR)法和贝叶斯置信递进神经网络(BCPNN)法对ADE信号进行挖掘。结果 共得到ADE报告 24 141 份，检测到ADE信号 273 个，共涉及 21 个系统器官分类(SOC)，其中各类检查(62 个，22。71%)、胃肠系统疾病(41 个，15。02%)、肝胆系统疾病(17 个，6。23%)等涉及信号数较多。首选语(PT)报告例数排前 3 的分别为腹泻(4 065 例)、食欲减退(1 648 例)、高血压(1 395 例);ADE信号强度排前 3 的为肛门直肠溃疡、毛发颜色改变、睫毛脱色。结论 培唑帕尼ADE信号挖掘结果与其药品说明书记载基本一致。对于培唑帕尼药品说明书中未提及的部分ADE，如肿块(多部位)、黄视症等，目前虽尚无研究证实与使用该药有直接关联，但临床使用时也需留意。
Signal Mining of Adverse Drug Events of Pazopanib Based on FAERS
Objective To provide a reference for the rational use of pazopanib in clinical practice.Methods The adverse drug event(ADE)reports with pazopanib as the primary suspicious drug in the FDA Adverse Event Reporting System(FAERS)from January 1,2009 to April 30,2023 were obtained,the ADE signals were mined by the reporting odds ratio(ROR)and Bayesian confidence propagation neural network(BCPNN)methods.Results A total of 24 141 ADE reports were obtained,and 273 ADE signals were detected,involving 21 system organ classifications(SOCs),in which various examinations(22.71%),gastrointestinal disorders(15.02%)and hepatobiliary disorders(6.23%)involving more signals,with 62,41,17 signals respectively.The top three preferred terms(PTs)in terms of reported cases were diarrhea(4 065 cases),anorexia(1 648 cases)and hypertension(1 395 cases).The top three ADEs in terms of signal intensity were anorectal ulcers,hair color changes and eyelash discoloration.Conclusion The results of pazopanib-related ADE signal mining are basically consistent with those recorded in the drug instructions.For some ADEs such as lumps(multiple parts)and xanthopsia that are not mentioned in the drug instructions of pazopanib,although there is currently no research to confirm their direct association with the use of the drug,attention should also be paid in clinical use.

pazopanibadverse drug eventFDA Adverse Event Reporting Systemsignal miningreporting odds ratioBayesian confidence propagation neural network

游宏勇、李卫平、王强

展开 >

中国人民解放军陆军军医大学附属第二医院,重庆 400037

培唑帕尼 药品不良事件 美国食品和药物管理局不良事件报告系统 信号挖掘 报告比值比法 贝叶斯置信递进神经网络法

重庆市临床药学重点专科建设项目

渝卫办发[2020]68号

2024

10.3969/j.issn.1006-4931.2024.12.024

中国药业
重庆市食品药品监督管理局

中国药业

CSTPCD
影响因子：1.369
ISSN：1006-4931
年,卷(期)：2024.33(12)