山西医科大学学报2024，Vol.55Issue(6)：798-803,6.DOI:10.13753/j.issn.1007-6611.2024.06.018
基于真实世界数据的阿米替林不良事件信号挖掘
Signal mining of Amitriptyline adverse events based on real-world data
摘要
Abstract
Objective To mine potential adverse drug event signals of Amitriptyline based on real-world data(RWD)for its rational clinical use and patients'medication safety.Methods Adverse drug event(ADE)report data from the FAERS database in 40 quarters from the first quarter of 2013 to the fourth quarter of 2022 were extracted for the study.The reporting odds ratio(ROR)method and the Medicines and Healthcare Products Regulatory Agency(MHRA)method were used to mine the medication use and analyze the Amitrip-tyline ADE reports that met the risk signal detection criteria.These reports were compared with the Chinese Amitriptyline drug instruc-tions and the U.S.National Library of Medicine to identify new ADEs and analyze their patterns.Results Amitriptyline was used as the primary suspect drug(PS)in 14 969 target events involving 3 169 patients.A total of 319 signals were detected,including various agent toxicities,suicide attempts,overdose,drug interactions and anticholinergic syndromes,etc.These events involved 27 system organs classes(SOC),focusing on various neurological disorders,psychiatric categories,various types of injuries,poisoning and oper-ational complications,etc.Conclusion The Amitriptyline ADE signals mined by FAERS are basically consistent with those described in the Chinese Amitriptyline drug instructions and the U.S.National Library of Medicine.In addition to the above-mentioned adverse events,the adverse events that are not included in Amitriptyline but have a stronger signal or a higher number of reports should also be paid more attention to in the process of clinical medication.The risk of medication should be assessed in a timely manner for effective prevention and safe medication of patients.关键词
阿米替林/药物不良事件/真实世界数据/FAERS/数据挖掘Key words
Amitriptyline/adverse drug events/real world data/FAERS/data mining分类
医药卫生引用本文复制引用
吕艳华,梁傲雪,李琦..基于真实世界数据的阿米替林不良事件信号挖掘[J].山西医科大学学报,2024,55(6):798-803,6.基金项目
国家社会科学基金一般项目(20BTQ064) （20BTQ064）
