广州医药2024，Vol.55Issue(5)：478-488,11.DOI:10.3969/j.issn.1000-8535.2024.05.005
基于FAERS数据库的奥马珠单抗不良事件信号挖掘
Omalizumab adverse event signal mining based on FAERS database
摘要
Abstract
Objective To use data mining method to detect the adverse reaction signal of omalizumab after marketing,and to provide reference for clinical safety and rational drug use.Methods In this study,the report odds ratio method(ROR)and Bayesian confidence propagation neural network(BCPNN)were used to conduct data mining and signal detection for omalizumab-related adverse event(ADE)reports from the FDA Adverse Event Reporting System(FAERS)from the first quarter of 2004 to the second quarter of 2023.Results Through data mining and signal detection,186,353 reports of ADE involving omalizumab were extracted,involving 45,383 patients.Among these reports,the proportion of women(65.31%)was much higher than that of men(24.97%).The main reporting countries were the United States(64.93%)and Canada(11.96%).consumers(41.35%)and doctors(36.97%)were the main groups of reporters.The study identified 621 ADE positive signals across 25 system organ classes(SOCs),including respiratory,chest,and mediastinal diseases(21.29%)and infectious and infectious diseases(10.91%).Of these,183 signals were assessed as high risk,including 57 new high-risk signals.These findings contribute to a more complete understanding of the safety and potential risks of omalizumab.Conclusions In the clinical application of omalizumab,in addition to the known adverse reactions mentioned in the drug description,special attention should be paid to potential adverse drug events,such as elevated blood pressure,elevated heart rate,intermediate insomnia,and postatic tachycardia syndrome.关键词
奥马珠单抗/药品不良事件/信号挖掘/合理用药/药品不良反应Key words
omalizumab/adverse drug events/signal mining/rational drug use/adverse drug reaction引用本文复制引用
卢妤,廖兆豪,任冠桦,吴四智,马为..基于FAERS数据库的奥马珠单抗不良事件信号挖掘[J].广州医药,2024,55(5):478-488,11.基金项目
国家自然科学基金资助项目(82204811) （82204811）
广州市科学技术局重点研发计划项目(202206010099) （202206010099）
