期刊文献+

基于FDA不良事件数据库阿司匹林相关痛风和高尿酸血症不良事件分析 被引量:2

Analysis of aspirin related gout and hyperuricaemia adverse events based on FDA adverse event database
下载PDF
导出
摘要 目的 利用美国食品药品监督管理局(FDA)不良事件报告系统(FAERS)数据库挖掘阿司匹林相关痛风和高尿酸血症的不良事件(AE)信号并进行分析,为其临床安全合理用药提供参考。方法 采用报告比值比(ROR)法对美国食品药品监督管理局公共数据开放项目OpenVigil 2.1数据库中于2004年1月1日—2021年9月30日上报的阿司匹林相关的痛风和高尿酸血症不良事件进行数据挖掘。结果 阿司匹林相关痛风的不良事件报告数为625例,报告比值比的95%CI:3.254(2.997-3.533)。高尿酸血症90个,报告比值比的95%CI:2.464(1.989-3.053)。女性信号强度高于男性,18~44岁年龄组信号强度高于其他年龄组。痛风和高尿酸血症不良事件中应用的阿司匹林均为小剂量。结论 小剂量阿司匹林是诱发痛风和高尿酸血症风险信号,阿司匹林诱发痛风信号强度高于高尿酸血症,二者可能为2个独立的不良事件,无相关性。女性和18~44岁组服用小剂量阿司匹林应低嘌呤饮食,注意监测血尿酸和痛风发作。 Objective The adverse events(AE) signals of aspirin related gout and hyperuricaemia were mined and analyzed by using the FDA adverse event reporting system(FAERS)database, so as to provide reference for clinical safe and rational drug use.Methods The reported odds ratio(ROR)method was used to mine the AE of gout and hyperuricaemia of aspirin reported in the open FDA OpenVigil 2.1 database from January 1,2004 to September 30,2021.Results The number of AE reported for aspirin associated gout was 625,with a 95% CI of ROR:3.254(2.997-3.533)and hyperuricaemia was 90,with a 95%CI of ROR:2.464(1.989-3.053).The signal intensity of female is higher than that of male, and that of 18~44 age group is higher than that of other age groups.Aspirin used in AE was low-dose.Conclusion Low dose aspirin is the risk signal of gout and hyperuricaemia.The signal intensity of gout is higher than that of hyperuricaemia.There is no correlation between them as independent AE.The female and 18~44 years old group taking low dose aspirin should take low purine diet, and pay attention to monitoring blood uric acid and gout attack.
作者 孙海燕 SUN Haiyan(Yantai Yuhuangding Hospital,Yantai 264000,China)
机构地区 烟台毓璜顶医院
出处 《药学研究》 CAS 2022年第12期838-840,共3页 Journal of Pharmaceutical Research
基金 烟台市科技计划(No.2020YD027)。
关键词 阿司匹林 美国食品药品监督管理局不良事件报告系统 痛风 高尿酸血症 信号挖掘 Aspirin FDA adverse event reporting system Gout Hyperuricaemia Signal mining
  • 相关文献

参考文献2

二级参考文献21

  • 1Guo XJ, Ye XF, Wang XX, eta/. Reporting patterns of ad- verse drug reactions over recent years in China: analysis from publications[J]. Expert Opin Drug Saf , 2015,14(2): 191- 198.
  • 2Hou Y, Ye X, Wu G, eta/. A comparison of dispmportional- ity analysis methods in national adverse drug reaction databas- es of China[J]. Expert Opin Drug Saf, 2014,13(7): 853- 857.
  • 3国家食品药品监督管理总局.国家食品药品监督管理局发布2010年药品不良反应报告[EB/OL].http://www.sda.gov.c,∥嘲1/a啪51/60952.htral.2011-04-25/2014-07-23.
  • 4国家食品药品监督管理总局.国家食品药品监督管理局发布2011年药品不良反应监测年度报告(EB/OL).http://www, sda. gov. cn/WSO1/CLO051/72190, html, 2012-05- 31/2014-07-31.
  • 5Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports [ J ]. Pharmacoepidemiol Drug Saf, 2001, 10(6) : 483-486.
  • 6Bate A, Lindquist M, Edwards IR, eta/. A Bayesian neural network method for adverse drug reaction signal generation [J]. Eur J Clin Pharnmcol, 1998, 54(4) : 315-321.
  • 7Norn GN, Hopstadius J, Bate A. Shrinkage observed-to-ex- pected ratios for robust and transparent large-scale pattern dis- covery[ J]. Stat Methods Med Res,2013, 22( 1 ) : 57-69.
  • 8Wang J, Ye XF, Guo X J, et al. Exploration of statistical shrinkage parameters of disproportionality methods in sponta- neous reporting system of China[ J]. Pharmaoepidemiol Drug Saf, 2015. in press, doi:lO, lO02/pds. 3811.
  • 9何清.高尿酸血症和痛风的病因与流行病学[J].中国临床医生杂志,2009,37(1):11-13. 被引量:79
  • 10孙亚林,李永昌,杜文民,叶小飞,贺佳.药物警戒中不相称性测定理论应用问题分析[J].药物流行病学杂志,2009,18(3):147-150. 被引量:13

共引文献30

同被引文献29

引证文献2

二级引证文献2

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