期刊文献+

利用医院信息系统监测分析药物性肝损伤 被引量:6

Monitoring and Analysis of Drug Induced Liver Injury by Using Hospital Information System
原文传递
导出
摘要 目的:研究利用医院信息系统监测分析药物性肝损伤的特点。方法:从医院信息系统数据库中提取2011年9月20-30日间丙氨酸氨基转移酶、门冬氨酸氨基转移酶、总胆红素、结合胆红素、碱性磷酸酶和γ-谷氨酰转移酶异常的住院患者资料,进行回顾性分析。结果:可能发生药物性肝损伤的有147例,主要分型为肝细胞性肝损伤。涉及11个科室、13类药物,居首位的是抗菌药。结论:本方法可有效挖掘药物性肝损伤发生的信息,使用计算机自动监测预警药品不良反应有着广泛的应用前景。 Objective:To study a way to monitor and analyze the characteristics of drug induced liver injury by using the hospital information system. Methods:The data of inpatients with abnormal ALT, AST, TB, CB, ALP, GGT were extracted from HIS database from Sept. 20, 2011 to Sept. 30, 2011 and analyzed retrospectively. Results: 147 drug induced liver injury incidents were concerned with 13 kinds of drugs and 11 departments. The major type of drug induced liver injury was hepatocellular injury and the major kind of drugs was antibiotics. Conclusion: The information of drug induced liver injury could be effectively obtained in this way. The technology of using computer automatically to monitor adverse drug reactions had its broad application prospect.
出处 《药物流行病学杂志》 CAS 2014年第2期88-91,共4页 Chinese Journal of Pharmacoepidemiology
关键词 医院信息系统 药品性肝损伤 药物不良反应 分析 Hospital information system Drug induced liver injury Adverse drug reaction Analysis
  • 相关文献

参考文献17

二级参考文献97

共引文献1064

同被引文献68

  • 1赵媛媛,章袁,王屏.药源性肝损伤133例的病例统计与分析[J].实用医药杂志,2013,30(12):1103-1105. 被引量:3
  • 2王宗敏,吴晓明.英国处方事件监测制度研究[J].中国药房,2005,16(24):1891-1892. 被引量:4
  • 3Miller AB, Hoogstraten B, Staquet M, et al. Reporting re- suits of cancer treatment[J]. Cancer, 1981,47(1 ) : 207-214.
  • 4Massimo DM, Ciro G, Natasha B, et al. Symptomatic toxicities experienced during anticancer treatment:Agreement between patient and physician reporting in three randomized trials [ J]. Journal of Clinical Oncology, 2015,26 : 1-7.
  • 5Harpaz R, DuMouchel W, Shah NH, et al. Novel data-mining methodologies for adverse drug event discovery and analysis[J]. Clin Pharmacol Ther, 2012, 91(6): 1010-1021.
  • 6Overhage JM, Ryan PB, Reich CG, et al. Validation of a common data model for active safety surveillance research[J]. J Am Med Inform Assoc, 2012, 19(1): 54-60.
  • 7Madigan D, Ryan PB, Schuemie M, et al. Evaluating the impact of database heterogeneity on observational study results[J]. Am J Epidemiol, 2013, 178(4): 645-651.
  • 8Hansen RA, Gray MD, Fox BI, et al. How well do various health outcome definitions identify appropriate cases in observational studies?[J]. Drug Saf, 2013, 36(Suppl 1): $27-$32.
  • 9Overhage JM, Ryan PB, Schuemie MJ, et al. Desideratum for evidence based epidemiology[J]. Drug Saf, 2013, 36(Suppl 1): $5-S14.
  • 10Madigan D, Stang PE, Berlin JA, et al. A systematic statistical approach to evaluating evidence from observational studies[J]. Armu Rev Stat Its Appli, 2014, 1(1): 11-39.

引证文献6

二级引证文献66

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