国内外基于真实世界数据的药品上市后安全性主动监测系统对比分析

doi:10.19803/j.1672-8629.20240376

中国药物警戒 ›› 2024, Vol. 21 ›› Issue (8): 892-899.
DOI: 10.19803/j.1672-8629.20240376

国内外基于真实世界数据的药品上市后安全性主动监测系统对比分析

孙一鑫^1,2, 聂晓璐³, 王晓玲^1,2, 郭鹏^1,2,*

¹国家儿童医学中心,首都医科大学附属北京儿童医院临床研究中心,北京 100045;
²国家儿童医学中心,首都医科大学附属北京儿童医院药学部,北京 100045;
³国家儿童医学中心,首都医科大学附属北京儿童医院临床流行病学与循证医学中心,北京 100045

收稿日期:2024-06-04 出版日期:2024-08-15 发布日期:2024-08-21
通讯作者: ^*郭鹏,男,博士,研究员,临床药理学。E-mail: guopeng@bch.com.cn
作者简介:孙一鑫,女,博士,助理研究员,药物流行病学。
基金资助:
国家自然科学基金资助项目（82204149）; 中国药品监督管理研究会课题、国家药监局药品监管科学体系建设重点项目（RS2024G001）

Comparative research on active post-marketing drug safety surveillance systems worldwide based on real-world data

SUN Yixin^1,2, NIE Xiaolu³, WANG Xiaoling^1,2, GUO Peng^1,2,*

¹Clinical Research Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China;
²Department of Pharmacy, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China;
³Center for Clinical Epidemiology & Evidence-based Medicine, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing 100045, China

Received:2024-06-04 Online:2024-08-15 Published:2024-08-21

摘要/Abstract

摘要： 目的对比分析国内外基于真实世界数据（RWD）构建的药品上市后安全性主动监测系统,以期为我国建立有效可行的全国性系统提供参考。方法检索与梳理全球药品主动监测系统的文献资料,使用框架整合法确定分析框架,编码与标绘有关组织架构、数据特征、运行机制等方面的内容,归纳异同点并总结主动监测模式。结果纳入基于RWD开展主动监测的系统/组织共12个,9个位于欧美国家,1个位于亚洲地区,我国有2个,分别为中国医院药物警戒系统（CHPS）和临床药物不良事件主动监测与智能评估警示系统（ADE-ASAS-Ⅱ）。经过信息整合,从6个维度总结主动监测模式：①组织模式归纳出3类,分别为政府（与学术界、药品生产企业）主导的合作体模式、政府直接管理模式,以及学术联盟协商模式;②数据整合与共享模式包括通用数据模型、通用研究方案与集中式数据库;③数据获取模式可分为2种形式,从数据库自动采集,以及针对目标人群或专项研究进行人工收集;④数据治理模式涉及匿名化、结构化与标准化处理;⑤数据储存与传输模式可分为分布式数据网络与中央数据库存储;⑥数据分析模式包括分布式计算与集中式分析2类。结论目前欧美国家利用RWD开展药品安全性主动监测模式相对成熟,我国在借鉴先进经验的同时,应探索符合本国实际情况的主动监测系统,为患者用药安全保驾护航。

关键词: 药品, 上市后安全性, 主动监测, 药物警戒, 真实世界数据

Abstract: Objective s To compare and analyze the active post-marketing safety surveillance practices based on real-world data (RWD) worldwide, in order to provide the reference for establishing an effective and feasible nationwide active surveillance system in China. Methods The literature on active surveillance systems around the world were searched and reviewed. The framework synthesis was used to determine the analytical framework, coding and mapping the information related to organizational structure, data characteristics, and operation mechanism. The similarities and differences of the active surveillance model were summarized. Results A total of 12 systems/organizations that conduct active surveillance based on RWD were included in this study, 9 of which are located in Europe and the United States, and in Asia. There are two Chinese systems, the China Hospital Pharmacovigilance System (CHPS) and the Adverse Drug Event Active Surveillance and Assessment System-Ⅱ (ADE-ASAS-Ⅱ). After integrating the information, the active monitoring model was summarized in six dimensions: (i) The organizational model was divided into three categories, including the government (with academia and pharmaceutical companies) led consortium, the direct government management, and the academic alliance consultation; (ii) The data integration and sharing model included the common data model, the common study protocol, and the centralized database; (iii) The data collection model could be divided into two forms: automatic collection from databases, and manual collection for target populations or specialized studies; (iv) The data management model included anonymizing, structuring and standardizing processes; (v) The data storage and transmission model can be categorized into the distributed data networks and the central database storage; (vi) Correspondingly, the data analysis model was divided into two categories: distributed computing and centralized analysis. Conclusion At present, the pattern of active surveillance for drug safety based on RWD are relatively mature in developed countries. While learning from those advanced experiences, we should also explore the active surveillance in line with the actual situation of China, to protect the medication safety of patients.

Key words: drug, post-marketing safety, active surveillance, pharmacovigilance, real-world data.

中图分类号:

R951
R994.1

孙一鑫, 聂晓璐, 王晓玲, 郭鹏. 国内外基于真实世界数据的药品上市后安全性主动监测系统对比分析[J]. 中国药物警戒, 2024, 21(8): 892-899.

SUN Yixin, NIE Xiaolu, WANG Xiaoling, GUO Peng. Comparative research on active post-marketing drug safety surveillance systems worldwide based on real-world data[J]. Chinese Journal of Pharmacovigilance, 2024, 21(8): 892-899.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://zgywjj.magtechjournal.com/CN/10.19803/j.1672-8629.20240376

https://zgywjj.magtechjournal.com/CN/Y2024/V21/I8/892

参考文献

[1] FERNER RE, MCGETTIGAN P.Adverse drug reactions[J]. BMJ, 2018, 363: k4051.
[2] MCMAHON AW, DAL PG.Assessing drug safety in children - the role of real-world data[J]. N Engl J Med, 2018, 378(23): 2155-2157.
[3] GAO YJ, ZHAO X, BAI TK, et al.Discovery and identification strategy for drug safety risks based on big data monitoring of adverse reactions[J]. Chinese Journal of Pharmacovigilance(中国药物警戒), 2024, 21(1): 1-5, 14.
[4] ALOMAR M, TAWFIQ AM, HASSAN N, et al.Post marketing surveillance of suspected adverse drug reactions through spontaneous reporting: current status, challenges and the future[J]. Ther Adv Drug Saf, 2020, 11: 2042098620938595.
[5] PLATT R, BROWN JS, ROBB M, et al.The FDA sentinel initiative - an evolving national resource[J]. N Engl J Med, 2018, 379(22): 2091-2093.
[6] KANG B, YOON J, KIM HY, et al.Deep-learning-based automated terminology mapping in OMOP-CDM[J]. J Am Med Inform Assoc, 2021, 28(7): 1489-1496.
[7] HOU YF, SONG HB, LIU HL, et al.Practice and discussion on active surveillance by China Hospital Pharmacovigilance System[J]. Chinese Journal of Pharmacovigilance(中国药物警戒), 2019, 16(4): 212-214.
[8] ONG N, LUCIEN A, LONG JC, et al.What do healthcare staff think about the quality and safety of care provided to children and young people with an intellectual disability? A qualitative study using the framework method of analysis[J]. BMJ Open, 2023, 13(7): e071494.
[9] BALL R, ROBB M, ANDERSON SA, et al.The FDA’s sentinel initiative-a comprehensive approach to medical product surveillance[J]. Clin Pharmacol Ther, 2016, 99(3): 265-268.
[10] SIVESIND TE, RUNION T, BRANDA M, et al.Dermatologic research potential of the Observational Health Data Sciences and Informatics (OHDSI) network[J]. Dermatology, 2022, 238(1): 44-52.
[11] REYNOLDS RF, KURZ X, DE GROOT MC, et al.The IMI PROTECT project: purpose, organizational structure, and procedures[J]. Pharmacoepidemiol Drug Saf, 2016, 25(Suppl 1): 5-10.
[12] YANG L, TIAN LJ, LUO G.Comparative research and enlightenment on active post-marketing drug safety surveillance systems from the global perspective[J]. Science and Technology Management Research(科技管理研究), 2017, 7: 182-190.
[13] GE SY, LIANG Y.Comparative study on active drug safety surveillance practice of China and the United States[J]. Chinese Journal of Medicinal Guid(中国医药导刊), 2023, 4: 355-361.
[14] YANG L, WU D.Research on the mode, core mechanism and implementation path of collaborative governance of drug safety in China[J]. Journal of Shenyang Pharmaceutical University(沈阳药科大学学报), 2021, 9: 964-968.
[15] SUISSA S, HENRY D, CAETANO P, et al.CNODES: the Canadian network for observational drug effect studies[J]. Open Med, 2012, 6(4): e134-140.
[16] COLOMA PM, SCHUEMIE MJ, TRIFIRO G, et al.Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project[J]. Pharmacoepidemiol Drug Saf, 2011, 20(1): 1-11.
[17] ANDERSEN M, BERGMAN U, CHOI NK, et al.The Asian Pharmacoepidemiology Network (AsPEN): promoting multi-national collaboration for pharmacoepidemiologic research in Asia[J]. Pharmacoepidemiol Drug Saf, 2013, 22(7): 700-704.
[18] VERKERK K, VOEST EE.Generating and using real-world data: a worthwhile uphill battle[J]. Cell, 2024, 187(7): 1636-1650.
[19] LIU F, PANAGIOTAKOS D.Real-world data: a brief review of the methods, applications, challenges and opportunities[J]. BMC Med Res Methodol, 2022, 22(1): 287.
[20] HENKE E, ZOCH M, PENG Y, et al.Conceptual design of a generic data harmonization process for OMOP common data model[J]. BMC Med Inform Decis Mak, 2024, 24(1): 58.
[21] FDA’s Sentinel Initiative. Sentinel Common Data Model v8.1.0[EB/OL]. (2024-02-13)[2024-02-18]. https://sentinelinitiative.org/methods-data-tools/sentinel-common-data-model.
[22] Observational Health Data Sciences and Informatics. OMOP Common Data Model v5.4[EB/OL]. (2023-06-13)[2024-02-18]. https://ohdsi.github.io/CommonDataModel/cdm54.html.
[23] REISINGER SJ, RYAN PB, O’HARA DJ, et al. Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases[J]. J Am Med Inform Assoc, 2010, 17(6): 652-662.
[24] LAI EC, RYAN P, ZHANG Y, et al.Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges[J]. Clin Epidemiol, 2018, 10: 875-885.
[25] PLATT RW, HENRY DA, SUISSA S.The Canadian Network for Observational Drug Effect Studies (CNODES): reflections on the first eight years, and a look to the future[J]. Pharmacoepidemiol Drug Saf, 2020, 29(Suppl 1): 103-107.
[26] National Health Commission of the People’s Republic of China. Notice on the further enhancement of medication safety management to improve the rational use of medication[EB/OL]. (2022-07-27)[2023-11-23]. http://www.gov.cn/zhengce/zhengceku/2022-07/30/content_5703604.htm.
[27] The State Council of the People’s Republic of China.“The 14th Five-Year Plan”National drug safety and promotion of high-quality development plan[EB/OL]. (2022-01-02)[2023-11-22]. http://www.gov.cn/xinwen/2022-01/02/content_5667258.htm.

国内外基于真实世界数据的药品上市后安全性主动监测系统对比分析

Comparative research on active post-marketing drug safety surveillance systems worldwide based on real-world data

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	陆德, 许文艳, 刘瑾欣, 裴宇盛. 美国食品药品监督管理局警告信中细菌内毒素问题探讨[J]. 中国药物警戒, 2024, 21(8): 857-862.
[2]	魏芬芳, 吴文宇, 李丽敏, 唐碧雨, 吴建茹. 国外药品获益-风险评价方法概述与启示[J]. 中国药物警戒, 2024, 21(8): 900-905.
[3]	王玉玺, 张宗林, 王颖丽, 陈亚飞, 李俊生, 霍艳飞, 谢彦军, 刘文启. 560例脉络舒通丸不良反应/事件报告分析[J]. 中国药物警戒, 2024, 21(8): 921-924.
[4]	赵俊, 董丽, 王洋, 梁瑜, 杨雪, 李恩泽. 艾司洛尔不良反应/事件报告分析[J]. 中国药物警戒, 2024, 21(8): 925-930.
[5]	张乾, 恽渊, 何恋. 1例利培酮致高泌乳素血症患者的药学监护[J]. 中国药物警戒, 2024, 21(8): 936-938.
[6]	晁滢, 张学梅, 张成健, 王贝. 注射用头孢曲松钠致儿童输尿管结石及胆囊结石1例分析[J]. 中国药物警戒, 2024, 21(8): 943-945.
[7]	郭海嬿, 何波, 闫鸿丽, 杨雪婷. 稳心颗粒致持续性呃逆1例分析[J]. 中国药物警戒, 2024, 21(8): 946-948.
[8]	王丹, 董铎, 田春华. 我国药物警戒制度发展历程和思考[J]. 中国药物警戒, 2024, 21(7): 735-740.
[9]	邵波, 郭晓昕. 中药药物警戒回顾与思考[J]. 中国药物警戒, 2024, 21(7): 741-745.
[10]	朱彦, 李庆娜, 朱兰, 谢江安, 姚克宇, 黄光瑞, 杨啸林, 关健. MedDRA在中国药品监测应用实施的现状分析及建议[J]. 中国药物警戒, 2024, 21(7): 746-751.
[11]	阿拉腾花, 王振兴, 张晓朦, 世伟, 席成伟, 王聪慧, 王立群, 张冰. 基于药品不良反应监测数据的心脏器官伤害风险信号挖掘分析[J]. 中国药物警戒, 2024, 21(7): 752-758.
[12]	刘亚迪, 张晓朦, 蔡海丽, 陈思颖, 王雨, 张冰. 阿霉素心脏毒性的中药防治用药分析及证素实证研究[J]. 中国药物警戒, 2024, 21(7): 759-764.
[13]	蔡海丽, 张晓朦, 刘亚迪, 刘淑佳, 高福君, 王雨, 张冰. 药源性心脏毒性模型的构建与评价进展[J]. 中国药物警戒, 2024, 21(7): 765-770.
[14]	程毅菁, 李涛, 刘军廷, 黄贵民, 钟旭丽, 林爱华, 潘守东, 李龙, 白东升, 占小俊, 刘振江, 任韡, 邰隽. 基于真实世界数据的注射用尖吻蝮蛇血凝酶儿童用药安全性评价[J]. 中国药物警戒, 2024, 21(7): 787-790.
[15]	赵杰, 高明娥, 常虹, 范惠霞. 复方聚乙二醇电解质散致患儿过敏样反应2例分析[J]. 中国药物警戒, 2024, 21(7): 816-818.