世界卫生组织药品不良反应数据库全球信号管理方法简述

doi:10.19803/j.1672-8629.20240373

摘要/Abstract

摘要： 世界卫生组织（WHO）药品不良反应病例报告数据库（VigiBase）自 1978 年起由乌普萨拉监测中心（UMC）开发和维护。目前VigiBase 保存着超过 3 900 万份匿名的患者疑似药品和疫苗不良反应报告（截至 2024 年 6 月）。为支持世界卫生组织国际药物监测计划（WHO PIDM）成员国制定、实施和加强安全监测计划,UMC开发了多个系统、工具和方法,以促进 VigiBase 的使用。本文描述了6种vigi方法（vigiMethods）: vigiGrade、vigiMatch、vigiRank、vigiPoint、vigiGroup 和 vigiVec。VigiBase 数据来源依靠主动报告的药品不良反应,但药品不良反应漏报是一个主要问题。为提高大型数据库（如 VigiBase）中病例处理的效率、质量和能力,人工智能增强方法正在不断发展,以满足管理日益增长的数据量需求。继续开发现有的和新的方法,将 VigiBase 和其他数据库中的报告内容转化为有价值的信息,对于全球所有国家的患者更安全地使用药物和疫苗至关重要。

关键词: 药品不良反应数据库, 世界卫生组织, 全球, 不良反应, 信号, 安全, 数据库, 管理, 方法

Abstract: The WHO database of adverse event reports for medicines and vaccines, VigiBase, has been developed and maintained by UMC since 1978. VigiBase now holds over 39 million anonymised reports of suspected adverse effects of medicines and vaccines suffered by patients (as of June 2024). To support of members of the WHO Programme for International Drug Monitoring (WHO PIDM) in developing, implementing, and strengthening safety surveillance programmes, UMC has developed several systems, tools and methods to facilitate the use of VigiBase. This paper describes six such vigi Methods; vigiGrade, vigiMatch, vigiRank, vigiPoint, vigiGroup, vigiVec. VigiBase depends on unsolicited reporting of adverse drug effects and, regretfully, underreporting of adverse drug effects continues to be a major issue. Artificial intelligence methods for augmentation to increase efficiency, quality, and capability of case processing in the large databases such as VigiBase are evolving to meet the higher demands to manage the increasing volume of safety data. Continued development of existing and new methods to translate the information in the reports in VigiBase and other databases into valuable knowledge will be paramount for safer use of medicines and vaccines for patients in all countries globally.

Key words: VigiBase, WHO, global, adverse drug reactions, signal, safety, database, management, methods

中图分类号:

R952
R994.11

彼得·希尔姆斯特伦姆, 杰弗里·鲍林, 岳群英, G.尼克拉斯·诺伦. 世界卫生组织药品不良反应数据库全球信号管理方法简述[J]. 中国药物警戒, 2024, 21(7): 836-840.

HJELMSTRÖM Peter, BOWRING Geoffrey, YUE Qun-Ying, NORÉN G. Niklas. Methods for signal management using the global safety database VigiBase[J]. Chinese Journal of Pharmacovigilance, 2024, 21(7): 836-840.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://zgywjj.magtechjournal.com/CN/10.19803/j.1672-8629.20240373

https://zgywjj.magtechjournal.com/CN/Y2024/V21/I7/836

参考文献

[1] LINDQUIST M, EDWARDS IR.The WHO programme for international drug monitoring, its database, and the technical support of the Uppsala Monitoring Centre[J]. J Rheumatol, 2001, 28: 1180-1187.
[2] VENULET J, BORDA MH.WHO’s International drug monitoring - the formative years, 1968-1975[J]. Drug Saf, 2010, 33: e1-e23.
[3] BROWN EG, WOOD L, WOOD S.The medical dictionary for regulatory activities (MedDRA)[J]. Drug Saf, 1999, 20: 109-117.
[4] LAGERLUND O, STRESE S, FLADVAD M, et al.WHODrug: a global, validated and updated dictionary for medicinal information[J]. Ther Innov Regul Sci, 2020, 54: 1116-1122.
[5] IAN H.Ralph Edwards: Rare Events - The Inside Story of a Worldwide Quest for Safer Medicines[M]. Berlin: Springer, 2003: 302.
[6] BERGVALL T, NORÉN GN, LINDQUIST M. vigiGrade: a tool to identify well-documented individual case reports and highlight systematic data quality issues[J]. Drug Saf, 2014, 37: 65-77.
[7] PLESSIS L, GÓMEZ A, GARCÍA N, et al. Lack of essential information in spontaneous reports of adverse drug reactions in Catalonia - a restraint to the potentiality for signal detection[J]. Eur J Clin Pharmacol, 2017, 73: 751-758.
[8] FERNANDEZ-FERNANDEZ C, LÁZARO-BENGOA E, FERNÁNDEZ-ANTÓN E. et al. Quantity is not enough: completeness of suspected adverse drug reaction reports in Spain- differences between regional pharmacovigilance centres and pharmaceutical industry[J]. Eur J Clin Pharmacol, 2020, 76, 1175-1181.
[9] TSUCHIYA M, OBARA T, MIYAZAKI M, et al.The quality assessment of the Japanese Adverse Drug event Report database using vigiGrade[J]. Int J Clin Pharm, 2020, 42: 728-736.
[10] WAKAO R, TAAVOLA H, SANDBERG L, et al.Data-driven identification of adverse event reporting patterns for Japan in VigiBase, the WHO Global Database of Individual Case Saf Reports[J]. Drug Saf, 2019, 42: 1487-1498.
[11] MATLALA MF, LUBBE MS, STEYN H, et al.The completeness of adverse drug reaction reports in South Africa: An analysis in VigiBase[J]. Afr J Prim Health Care Fam Med, 2023, 15: e1-e9.
[12] JOKINEN J, BERTIN D, DONZANTI B, et al.Industry assessment of the contribution of patient support programs, market research programs, and social media to patient safety[J]. Ther Innov Regul Sci, 2019, 53: 736-745.
[13] LEE I, JOKINEN JD, CRAWFORD SY, et al.Exploring completeness of adverse event reports as a tool for signal detection in pharmacovigilance[J]. Ther Innov Regul Sci, 2021, 55: 142-151.
[14] DITTRICH ATM, SMEETS NJL, DE JONG EFM, et al.Quality of active versus spontaneous reporting of adverse drug reactions in pediatric patients: relevance for pharmacovigilance and knowledge in pediatric medical care[J]. Pharmaceuticals(Basel), 2022, 15: 1148.
[15] LEITZEN S, DUBRALL D, TONI I, et al.Adverse drug reactions in children: comparison of reports collected in a pharmacovigilance project versus spontaneously collected ADR Reports[J]. Paediatr Drugs, 2023, 25: 203-215.
[16] NORÉN GN, ORRE R, BATEA. Hit-miss model for duplicate detection in the WHO Drug Safety Database[C]. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, 2005.
[17] NORÉN N, ORRE R, BATE A, et al. Duplicate detection in adverse drug reaction surveillance[J]. Data Min Knowl Discov, 2007, 14: 305-328.
[18] TREGUNNO PM, FINK DB, FERNANDEZ-FERNANDEZ C, et al.Performance of probabilistic method to detect duplicate individual case safety reports[J]. Drug Saf, 2014, 37: 249-258.
[19] FELLEGI IP, SUNTER AB.A theory for record linkage[J]. J Am Stat Assoc, 1969, 64: 1183-1210.
[20] COPAS JB, HILTON FJ.Record linkage: statistical models for matching computer records[J]. J R Stat Soc Ser A Stat Soc, 1990, 153: 287-320.
[21] CASTER O, JUHLIN K, WATSON S, et al.Improved statistical signal detection in pharmacovigilance by combining multiple strength-of-evidence aspects in vigiRank[J]. Drug Saf, 2014, 37: 617-628.
[22] CASTER O, SANDBERG L, BERGVALL T, et al.vigiRank for statistical signal detection in pharmacovigilance: First results from prospective real-world use[J]. Pharmacoepidemiol Drug Saf, 2017, 26: 1006-1010.
[23] STRANDELL J. CASTER O, HOPSTADIUS J, et al.The development and evaluation of triage algorithms for early discovery of adverse drug interactions[J]. Drug Saf, 2013, 36: 371-388.
[24] STAR K, SANDBERG L, BERGVAL T, et al.Paediatric safety signals identified in VigiBase: Methods and results from Uppsala Monitoring Centre[J]. Pharmacoepidemiol Drug Saf, 2019, 28: 680-689.
[25] WATSON S, CHANDLER RE, TAAVOLA H, et al.Safety concerns reported by patients identified in a collaborative signal detection workshop using VigiBase: results and reflections from lareb and Uppsala Monitoring Centre[J]. Drug Saf, 2018, 41: 203-212.
[26] JUHLIN K, STAR K, NORÉN GN. A method for data-driven exploration to pinpoint key features in medical data and facilitate expert review[J]. Pharmacoepidemiol Drug Saf, 2017, 26: 1256-1265.
[27] NORÉN GN, HOPSTADIUS J, BATE A. Shrinkage observed-to-expected ratios for robust and transparent large-scale pattern discovery[J]. Stat Methods Med Res, 2013, 22: 57-69.
[28] BERHE DF, JUHLIN K, STAR K, et al.Adverse drug reaction reports for cardiometabolic drugs from sub-Saharan Africa: a study in VigiBase[J]. Trop Med Int Health, 2015, 20(6): 797-806.
[29] WAKAO R, TAAVOLA H, SANDBERG L, et al.Data-driven identification of adverse event reporting patterns for Japan in Vigibase, the WHO Global Database of individual case safety reports[J]. Drug Saf, 2019, 42: 1487-1498.
[30] EKHART C, VAN HUNSEL F, VAN PUIJENBROEK E, et al.Post-marketing safety profile of vortioxetine using a cluster analysis and a disproportionality analysis of global adverse event reports[J]. Drug Saf, 2022, 45: 145-153.
[31] NORÉN GN, MELDAU EL, CHANDLER RE. Consensus clustering for case series identification and adverse event profiles in pharmacovigilance[J]. Artif Intell Med, 2021, 122: 102199.
[32] DEMPSTER AP, LAIRD NM, RUBIN DB.Maximum likelihood from incomplete data via the EM algorithm[J]. J R Stat Soc Series B Stat Methodol, 1977, 39: 1-38.
[33] NORÉN GN, MELDAU EL, CHANDLER RE. Consensus clustering for case series identification and adverse event profiles in pharmacovigilance[J]. Artif Intell Med, 2021, 122: 102199.
[34] CHANG J, BOYD-GRABER J, BLEI DM, et al.Reading tea leaves: how humans interpret topic models[C]. Advances in neural information processing systems, 2009: 288-296.
[35] EKHART C, HUNSEL F, CHANDLER R, et al.Post-marketing saf profile of vortioxetine using a cluster analysis and a disproportionality analysis of global adverse event reports[J]. Drug Saf, 2022, 45: 145-153.
[36] CHANDLER RE, JUHLIN K, FRANSSON J, et al.Current safety concerns with human papillomavirus vaccine: a cluster analysis of reports in VigiBase[J]. Drug Saf, 2017, 40: 81-90.
[37] RUDOLPH A, MITCHELL J, BARRETT J, et al. Global safety monitoring of COVID-19 vaccines: how pharmacovigilance rose to the challenge[J]. Ther Adv Drug Saf, 2022, 13: 2042098 6221118972.
[38] GATTEPAILLE LM.Using the WHO database of spontaneous reports to build joint vector representations of drugs and adverse drug reactions, a promising avenue for pharmacovigilance[C]. IEEE International Conference on Healthcare Informatics (ICHI), 2019: 1-6.
[39] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26: 1310.4546.
[40] MIKOLOV T, CHEN K, CORRADO G, et al.Efficient estimation of word representations in vector space[J]. Computer Science, 2013: 1301.3781.
[41] HAZELL L, SHAKIR SAW.Under-reporting of adverse drug reactions: a systematic review[J]. Drug Saf, 2006, 29: 385-396.
[42] BEELER PE, STAMMSCHULTE T, DRESSEL H, et al.Hospitalisations related to adverse drug reactions in Switzerland in 2012-2019: characteristics, in-hospital mortality, and spontaneous reporting rate[J]. Drug Saf, 2023, 46: 753-763.