期刊文献+

基于FAERS数据库的5-羟色胺3受体拮抗剂相关心脏不良事件分析 被引量:1

Analysis of 5-hydroxytryptamine 3 receptor antagonists related cardiac adverse events based on FAERS database
原文传递
导出
摘要 目的分析5-羟色胺3受体拮抗剂(5-HT_(3)RA)相关心脏不良事件(AEs)发生特点,挖掘风险信号,为临床用药提供参考。方法基于美国食品药品监督管理局不良事件报告系统(FAERS)挖掘2004年第一季度至2022年第一季度数据,运用多种检测方法获取风险信号,进行分析比较。结果检测出昂丹司琼心脏AEs风险信号28个,帕洛诺司琼5个,多拉司琼4个,格拉司琼0个,包含部分新的AEs,涉及心律失常、心肌疾病、心脏瓣膜疾病和血压异常等类型。校正后心电图QT间期延长、心动过缓、室性期外收缩和血压降低是多种药品的风险信号,心脏呼吸骤停是多拉司琼的风险信号。结论5-HT_(3)RA可能与多种心脏AEs相关,特别是心律失常,有必要开展AEs自动监测,探索真实世界的发生率和相关危险因素。 AIM To analyze the characteristics of cardiac adverse events(AEs)related to 5-hydroxytryptamine 3 receptor antagonists(5-HT_(3)RA),and explore the risk signals in order to provide reference for clinical medication.METHODS A variety of data mining methods were used to obtain risk signals for analysis and comparison based on the data of FDA Adverse Event Reporting System from the first quarter of 2004 to the first quarter of 2022.RESULTS Totally 28 cardiac AEs risk signals of ondansetron,5 of palonosetron,4 of dolasetron and 0 of granisetron were detected with new AEs included involving cardiac arrhythmias,myocardial disorders,cardiac valve disorders and blood pressure abnormal,etc.Corrected QT interval prolongation,bradycardia,ventricular extras ystole and blood pres sure decreased were multi-drug risk signals,while cardio-respiratory arrest was a risk signal of dolasetron.CONCLUSION 5-HT_(3)RA may lead to various cardiac AEs,especially arrhythmia.AEs automatic monitoring is necessary to conducted for exploring the incidence and risk factors in real world.
作者 李鹏 郭代红 郭海丽 伏安 赵安琪 高奥 LI Peng;GUO Daihong;GUO Haili;FU An;ZHAO Anqi;GAO Ao(Medical School of Chinese PLA,Beijing 100853,China;Department of Pharmacy,Medical Supplies Center of Chinese PLA General Hospital,Beijing 100853,China)
出处 《中国临床药学杂志》 CAS 2023年第3期166-171,共6页 Chinese Journal of Clinical Pharmacy
基金 中国研究型医院学会临床重点药品的使用监测和评价研究专项(编号Y2022FH-YWPJ01)
关键词 5-羟色胺3受体拮抗剂 心脏不良事件 信号挖掘 药物警戒 5-hydroxytryptamine 3 receptor antagonists cardiac adverse events signal mining pharmacovigilance
  • 相关文献

参考文献4

二级参考文献86

  • 1王屏,卜一珊.药源性心律失常156例统计分析[J].中国药事,2006,20(2):127-128. 被引量:6
  • 2Moore N, Thiessard F, Begaud B. The history of disproportional- ity measures (reporting odds ratio, proportional reporting rates) in spontaneous reporting of adverse drug reactions [J]. Phar- macoepidemiol Drug Saf, 2005, 14(4) : 285.
  • 3Rahul S, Amitava M. An urn model with applications to database performance evaluation [ J ]. Computers &Operations Research, 1997, 24(4) : 289.
  • 4Poluzzi E, Raschi E, Motola D,et al. De Ponti F. Antimicrobi- als and the risk of torsades de pointes : the contribution from data mining of the US FDA Adverse Event Reporting System [J]. Drug Saf, 2010, 33(4) : 303.
  • 5Woo E J, Ball R, Burwen DR,et al. Effects of stratification on da- ta mining in the us vaccine adverse event reporting system [J].DrugSaf, 2008, 31 (8) :667.
  • 6Manfred Hauben, Sebastian Horn , Lester Reich Potential. Use of Data-Mining Algorithms for the Detection of surprise Adverse Drug Reactions[J]. DrugSaf, 2007, 30 (2): 143.
  • 7Bate A, Lindquist M, Edwards IR,et al. A Bayesian neural net- work method for adverse drug reaction singal generation[J]. Eur J Clin Pharmacol, 1998,54 ( 4 ) : 315.
  • 8Man YP, Dukyong Y. A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database [ J ]. Pharmacoepidemiol Drug Saf, 2011, 20 ( 6 ) :598.
  • 9Kim J, Kim M, Ha JH,et al. Signal detection of methylphenldate by comparing a spontaneous reportingdatabase with a claims data- base [ J ]. Regul Toxicol Pharmacol, 2011, 61 (2) : 154.
  • 10Hochberg AM, Hauben M, Pearson RK,et al. An evaluation of three signal-detection algorithms using a highly inclusive refer- ence event database[ J]. Drug Saf, 2009, 32 (6) :509.

共引文献53

同被引文献3

引证文献1

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部