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目的通过对利妥昔单抗相关不良反应进行信号挖掘研究,为临床合理安全用药提供参考。方法采用报告比值比法(reporting odds ratio,ROR)和贝叶斯置信度递进神经网络法(Bayesian confidence propagation neural network,BCPNN),对美国不良事件报告系统(FDA Adverse Event Reporting System,FAERS)2014年第一季度~2015年第四季度收集的报告进行分析,挖掘利妥昔单抗产生的相关不良反应信号。结果 ROR法和BCPNN法分别挖掘利妥昔单抗可疑ADR不良反应相关信号657个和43个,其中包括药品说明书中未出现的新的可疑ADR信号分别为68个(ROR法,筛选条件:95%CI-排名前300位且ROR值大于2.5)和10个(BCPNN法)。结论利妥昔单抗可疑ADR信号的挖掘,可以为国内临床合理安全用药提供参考依据。
OBJECTIVE: To study the signal response of rituximab-related adverse reactions and provide a reference for clinical rational and safe drug use. Methods Reported odds ratio (ROR) and Bayesian confidence propagation neural network (BCPNN) were used to analyze the data of FDA Adverse Event Reporting System (FAERS) 2014 The reports collected from the first quarter of 2015 to the fourth quarter of 2015 were analyzed to reveal the adverse reaction signals generated by rituximab. Results ROR and BCPNN methods were used to mine 657 and 43 ADR-related signals of suspected ADR, respectively, including 68 new ADR signals (ROR method, 95 % CI - top 300 and ROR> 2.5) and 10 (BCPNN). Conclusion The detection of suspicious ADR signal of rituximab can provide a reference for the clinical rational and safe drug use in China.