中国药业2024，Vol.33Issue(12)：105-109,5.DOI:10.3969/j.issn.1006-4931.2024.12.024
基于FAERS的培唑帕尼不良事件信号挖掘
Signal Mining of Adverse Drug Events of Pazopanib Based on FAERS
摘要
Abstract
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.关键词
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Key words
pazopanib/adverse drug event/FDA Adverse Event Reporting System/signal mining/reporting odds ratio/Bayesian confidence propagation neural network分类
医药卫生引用本文复制引用
游宏勇,李卫平,王强..基于FAERS的培唑帕尼不良事件信号挖掘[J].中国药业,2024,33(12):105-109,5.基金项目
重庆市临床药学重点专科建设项目[渝卫办发[2020]68号]. （）
