摘要
目的 利用FAERS数据库检索儿童使用左氧氟沙星不良事件报告情况,并进行数据挖掘和分析,为临床安全用药提供参考.方法 通过OpenVigil 2.1平台检索2004年1月1日至2023年6月30日儿童使用左氧氟沙星不良事件报告数据,基于报告比值比法(ROR),对相关数据进行分析.结果 共检索到儿童应用左氧氟沙星不良事件报告484例,发现94个阳性风险信号.主要累及的系统器官为各种肌肉骨骼及结缔组织、胃肠系统、全身性及给药部位,前5位阳性信号为地霉菌感染(ROR=822.87)、肌腱疼痛(ROR=563.71)、溃疡分枝杆菌感染(ROR=352.65)、肌腱断裂(ROR=341.91)、免疫重建炎性综合征相关结核病(ROR=310.84).说明书中未提及的阳性信号排名前5分别为:溃疡分枝杆菌感染(ROR=352.65)、免疫重建炎性综合征相关结核病(ROR=310.84)、中枢神经系统结核瘤(ROR=102.85)、线状 IgA 病(ROR=82.68)、颅内压力增高(ROR=32.46).结论 左氧氟沙星用于儿童除已知的不良事件外,颅内压升高、结核相关疾病等不良反应风险信号强度较高,建议谨慎选择并加强相关安全性监测.
Abstract
Objective To search for the reports of adverse events of levofloxacin use in children using the FAERS database,and to mine and analyze the data to provide reference for safe clinical use.Methods The data reported of adverse events of levofloxacin use in children from January 1,2004 to June 30,2023 were retrieved through the OpenVigil 2.1 platform,and the relevant data were analyzed based on the reporting odds ratio(ROR)method.Results A total of 484 cases of adverse events of levofloxacin in children were retrieved,and 94 positive risk signals were found.The main systemic organs involved were various musculoskeletal and connective tissues,gastrointestinal system,systemic and administration sites,and the top five positive signals were Dimycodes infection(ROR=822.87),tendon pain(ROR=563.71),Mycobacterium ulcers infection(ROR=352.65),tendon rupture(ROR=341.91),and immune reconstitution inflammatory syndrome-related tuberculosis(ROR=310.84).The top five positive signals not mentioned in the label were Mycobacterium ulcerans infection(ROR=352.65),immune reconstitution inflammatory syndrome-associated tuberculosis(ROR=310.84),central nervous system tuberculoma(ROR=102.85),linear IgA disease(ROR=82.68),and increased intracranial pressure(ROR=32.46).Conclusion In addition to the known adverse events,levofloxacin is used in children,and the risk signal intensity of adverse reactions such as increased intracranial pressure and tuberculosis-related diseases is high,so it is recommended to carefully select and strengthen relevant safety monitoring.
基金项目
山东省自然科学基金博士基金(ZR2018BH045)
NETL
NSTL
万方数据