| 引用本文: | 陈琪莹,李毅敏,张吟.基于FAERS对阿利西尤单抗不良事件的分析研究[J].中国现代应用药学,2021,38(18):2256-2261. |
| CHEN Qiying,LI Yimin,ZHANG Yin.Analytical Study of Adverse Events of Alirocumab Based on FAERS[J].Chin J Mod Appl Pharm(中国现代应用药学),2021,38(18):2256-2261. |
|
| 摘要: |
| 目的 通过挖掘美国食品药品监督管理局不良事件呈报系统（FDA Adverse Event Reporting System，FAERS）中关于阿利西尤单抗的警戒信号，探讨该药潜在的不良反应，为临床安全用药提供依据。方法 检索FAERS数据库中有关阿利西尤单抗的不良事件，检索时限为2015年第3季度—2019年第4季度共18个季度，采用报告比值比法与比例报告比值法同时进行检测，获取该药的警戒信号。结果 经过多重筛查，报告比值比法与比例报告比值法获得的信号完全重叠共计41个，其中有28个不良事件信号说明书未收录。阿利西尤单抗较强的信号主要集中在肌痛、肌肉痉挛、流感、注射部位反应等，系统器官分类及信号个数最多的主要集中在肌肉骨骼和结缔组织疾病、神经系统疾病及感染和侵染等，同时也挖掘出该药可能引起神经感知功能障碍、血糖升高等警戒信号。通过对性别、年龄差异相关不良事件进一步分析，其中有性别差异的高危信号共11个，男性患者应警惕血肌酐磷酸激酶升高、肌无力等；女性患者应警惕尿路感染、注射部位反应等。有年龄差异的高危信号共16个，非老年患者应警惕血肌酐磷酸激酶升高、转氨酶升高等；老年患者应警惕言语障碍、步态障碍等。结论 利用FAERS进行警戒信号挖掘可较全面深入地分析研究阿利西尤单抗上市后的不良事件，进而有效地降低临床用药风险。 |
| 关键词: 阿利西尤单抗 波立达 比例失衡法 不良事件 数据挖掘 |
| DOI：10.13748/j.cnki.issn1007-7693.2021.18.011 |
| 分类号:R969.3 |
| 基金项目:福建省自然科学基金项目（2020J01213） |
|
| Analytical Study of Adverse Events of Alirocumab Based on FAERS |
|
CHEN Qiying, LI Yimin, ZHANG Yin
|
|
Department of Pharmacy, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
|
| Abstract: |
| OBJECTIVE To explore the potential adverse event of alirocumab by digging the warning signals of this drug in FDA adverse event reporting system(FAERS) and to provide a basis for clinical safe medication. METHODS The FAERS database was searched for adverse events on alirocumab, the search time limit is from the 3rd quarter of 2015 to the 4th quarter of 2019, a total of 18 quarters, the reporting odds ratio and proportional reporting ratio were used to detect at the same time to obtain the warning signs of the drug. RESULTS After multiple screening, the signals obtained by the ROR and the PRR completely overlapped a total of 41 signals and which 28 adverse events were not appear in the specification. The strong signal of alirocumab mainly concentrated in myalgia, muscle spasm, influenza, injection site reaction, etc. The system organ classification involved the most signals mainly in musculoskeletal and connective tissue disorders, nervous system disorders, and infections, etc. At the same time, it was also discovered that the drug may cause nerve perception dysfunction and increased blood glucose and other warning signs. Through further analysis of the adverse events related to gender and age differences, there were 11 high risk signals with gender differences. Male patients should be alert to increased blood creatine phosphokinase, muscle weakness, etc; female patients should be alert to urinary tract infections, injection site reactions, etc. There were 16 high risk signals with age differences. Non-elderly patients should be alert to increased blood creatine phosphokinase, increased hepatic enzyme, etc; elderly patients should be alert to dysphonia gait disturbance, etc. CONCLUSION The use of FAERS for warning signals mining can comprehensively and deeply analyze the adverse events after the listing of alirocumab, and then effectively reduce the risk of clinical medication. |
| Key words: alirocumab praluent measurse of disproportionality adverse events data mining |
