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Bibliometric analysis of active surveillance of post-marketing drug safety

Published on Oct. 01, 2024Total Views: 184 times Total Downloads: 29 times Download Mobile

Author: WANG Conghui 1 YANG Ziming 2, 3 WANG Zhenxing 1 SHI Wei 1 ALATENG Hua 1 XI Chengwei 1 PI  Songning 1 YUAN Xinmin 1 ZHAN Siyan 2, 3, 4, 5

Affiliation: 1. Inner Mongolia Pharmacovigilance Center, Hohhot 010010,China 2. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China 3. Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China 4. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China 5. Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100871, China

Keywords: Surveillance of post-marketing safety of drugs Active surveillance Pharmacovigilance Bibliometrics Visualization analysis

DOI: 10.12173/j.issn.1005-0698.202407027

Reference: WANG Conghui, YANG Ziming, WANG Zhenxing, SHI Wei, ALATENG Hua, XI Chengwei,PI Songning, YUAN Xinmin, ZHAN Siyan.Bibliometric analysis of active surveillance of post-marketing drug safety[J].Yaowu Liuxingbingxue Zazhi,2024, 33(9):1054-1063.DOI: 10.12173/j.issn.1005-0698.202407027.[Article in Chinese]

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Abstract

Objective  To conduct bibliometric visualization analyses of the literature domestic and overseas on active surveillance of post-marketing safety of drugs, aiming to display the current status and trend of hotspots in this field and to provide references for future research and the improvement of the Chinese management system of active surveillance.

Methods  The English and Chinese literature on active surveillance of post-marketing safety of drugs were searched in Web of Science and CNKI respectively and imported into CiteSpace 6.3.R2 software for the analysis of the number of publications, authors, institutions, and national cooperative networks, and the analysis of keyword co-occurrence, clustering and emergence.

Results  415 Chinese and 676 English literature were included, with an overall increasing trend in annual publication volume. The author collaboration network of Chinese literature was smaller than that of English literature, and the partnership network was sparse, with no strong centralized institution. Domestic drug regulatory agencies played an important role in the field, while drug companies' monitoring research on their own products was still relatively scarce. The research topic covered active surveillance systems, technical method research, and drug safety active surveillance practice research for specific drugs and diseases.

Conclusion  Countries worldwide have widely considered active surveillance of post-marketing drug safety. The heat of research activities in China has shown a significant growth trend. However, there is still a significant gap compared with the international frontiers. Further cooperation needs to be strengthened to promote the improvement of the active surveillance management system in China.

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References

1.袁林, 沈传勇, 主编. 风险识别、评估与控制[M]. 北京:中国医药科技出版社, 2022: 80.

2.王丹. 药品不良反应主动监测及其发展趋势[J]. 中国药物警戒, 2015, 12(10): 600-602, 610. [Wang D. Active surveillance of adverse drug reaction and its development trend[J]. Chinese Journal of Pharmacovigilance, 2015, 12(10): 600-602, 610.] DOI: 10.19803/j.1672-8629.2015.10.007.

3.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: 585480547. DOI: 10.1177/2042098620938595.

4.孙一鑫, 聂晓璐, 王晓玲, 等. 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 YX, Nie XL, Wang XL, et al. 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.] DOI: 10.19803/j.1672-8629.20240376.

5.王晶晶. 基于多维计量的图书情报学与教育学学科交叉可视化研究[D]. 太原: 山西财经大学, 2023. DOI: 10.27283/d.cnki.gsxcc.2023.001469.

6.Thompson DF, Walker CK. A  descriptive  and historical  review of  bibliometrics  with applications to medical science[J]. Pharmacotherapy, 2015, 35(6): 551-559. DOI: 10.1002/phar.1586.

7.马进原, 郑心怡, 张翠珍, 等. 基于Web of Science的免疫抑制药基因组学研究的文献计量分析[J]. 药物流行病学杂志, 2023, 32(4): 391-403. [Ma JY, Zheng XY, Zhang CZ, et al. Bibliometric analysis of the research of immunosuppressive pharmacogenomics based on Web of Science Database[J]. Chinese Journal of Pharmacoepidemiology, 2023, 32(4): 391-403.] DOI: 10.19960/j.issn.1005-0698.202304005.

8.Chen C. Science mapping:a systematic review of the literature[J]. J Data Inform Sci, 2017, 2(2): 1-40. DOI: 10.1515/jdis-2017-0006.

9.Barthelemy M, eds. Spatial networks: a complete introduction: from Graph theory and statistical physics to real-world applications[M]. Cham: Springer International Publishing, 2022: 65-108. DOI: 10.1007/978-3-030-94106-2_5.

10.赵安琪, 郭代红, 朱曼, 等. 基于HIS数据的药物相关肌肉不良反应自动监测模块建立与优化[J]. 中国药物应用与监测, 2023, 20(3): 176-179. [ Zhao AQ, Guo  DH, Zhu M, et al. Establishment and optimization of a module for automatic monitoring drug-associated muscle adverse reactions based on HIS[J]. Chinese Journal of Drug Application and Monitoring, 2023, 20(3): 176-179.] DOI: 10.3969/j.issn.1672-8157.2023.03.011.

11.李超, 郭代红, 朱曼, 等. 三唑类抗真菌药相关肝损伤不良事件监测及风险因素分析[J]. 中国临床药理学杂志, 2023, 39(8): 1182-1185. [Li C, Guo DH, Zhu M, et al. Monitoring of adverse events and risk factors of liver injury related to triazole antifungal drugs[J]. Chinese Journal of Clinical Pharmacology, 2023, 39(8): 1182-1185.] DOI: 10.13699/j.cnki.1001-6821.2023.08.025.

12.侯永芳, 宋海波, 刘红亮, 等. 基于中国医院药物警戒系统开展主动监测的实践与探讨[J]. 中国药物警戒, 2019, 16(4): 212-214. [ Hou YF, Song HB, Liu HL, et al. Practice and discussion on active surveillance by China Hospital Pharmacovigilance System [J]. Chinese Journal of Pharmacovigilance, 2019,16(4): 212-214.] DOI: 10.3969/j.issn.1672-8629.2019.04.005.

13.王连心, 杨硕, 王萌萌, 等. 100 249例热毒宁注射液儿童用药临床安全性的前瞻性、多中心、大样本医院集中监测[J]. 中国中药杂志, 2024, 49(12): 3396-3403. [Wang LX, Yang S, Wang MM, et al. Prospective, multicenter, large-scale hospital centralized monitoring of clinical safety of Reduning Injection in 100 249 children cases[J]. China Journal of Chinese Materia Medica, 2024, 49(12): 3396-3403.] DOI: 10.19540/j.cnki.cjcmm.20240306.501.

14.廖星, 谢雁鸣, 王连心, 等. 中成药上市后临床安全性医院集中监测报告规范的建议[J]. 中国中西医结合杂志, 2019, 39(2): 242-248. [Liao X, Xie YM, Wang  LX, et al. A proposed standards for reporting intensive hospital monitoring of traditional chinese patent medicines [J]. Chinese Journal of Integrated Traditional and Western Medicine, 2019, 39(2): 242-248.] DOI: 10.7661/j.cjim.20180523.052.

15.Biswas PN, Wilton LV, Shakir SA. The pharmacovigilance of mirtazapine: results of a prescription event monitoring study on 13 554 patients in England[J]. J psychopharmacol, 2003, 17(1): 121-126. DOI: 10.1177/0269881103017001716.

16.Meier CR, Jick H. Drug use and pulmonary death rates in increasingly symptomatic asthma patients in the UK[J]. Thorax, 1997, 52(7): 612-617. DOI: 10.1136/thx.52.7.612.

17.Chrischilles EA, Gagne JJ, Fireman B, et al. Prospective surveillance pilot of rivaroxaban safety within the US Food and Drug Administration Sentinel System[J]. Pharmacoepidemiol Drug Saf, 2018, 27(3): 263-271. DOI: 10.1002/pds.4375.

18.Ball R, Toh S, Nolan J, et al. Evaluating automated approaches to anaphylaxis case classification using unstructured data from the FDA Sentinel System[J]. Pharmacoepidemiol Drug Saf, 2018, 27(10): 1077-1084. DOI: 10.1002/pds.4645.

19.Patadia VK, Schuemie MJ, Coloma PM, et al. Can electronic health records databases complement spontaneous reporting system databases? a historical-reconstruction of the association of rofecoxib and acute myocardial infarction[J]. Front Pharmacol, 2018, 9: 594. DOI: 10.3389/fphar.2018.00594.

20.葛斯羿, 梁毅. 中美药品安全性主动监测实践对比研究[J]. 中国医药导刊, 2023, 25(4): 355-361. [Ge  SY, Liang  Y. Comparative study on active drug safety surveillance practice of China and the United States[J]. Chinese Journal of Medical Guide, 2023, 25(4): 355-361.] DOI: 10.3969/j.issn.1009-0959.2023.04.002.

21.吴广杰, 张程亮, 蔡沅璇, 等. 基于WHO药物警戒指标对我国药物警戒体系现状的评价[J]. 药物流行病学杂志, 2022, 31(5): 303-308. [ Wu GJ, Zhang CL, Cai YX, et al. Evaluation of current pharmacovigilance system in China: Based on WHO pharmacovigilance indicators[J]. Chinese Journal of Pharmacoepidemiology, 2022, 31(5): 303-308.] DOI: 10.19960/j.cnki.issn1005-0698.2022.05.003.

22.Natsiavas P, Gavriilidis GI, Linardaki Z, et al. Supporting active pharmacovigilance via IT Tools in the clinical setting and beyond: regulatory and management aspects[J]. Stud Health Technol Inform, 2020, 272: 342-345. DOI: 10.3233/SHTI200565.

23.Shin H, Cha J, Lee C, et al. The 2011-2020 trends of data-driven approaches in medical informatics for active pharmacovigilance[J]. Applied Sci, 2021, 11(5): 2249. DOI: 10.3390/app11052249.

24.Ward R, Hallinan CM, Ormiston-Smith D, et al. The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset[J]. PLoS One, 2024, 19(4): e0301557. DOI: 10.1371/journal.pone.0301557.

25.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]. JAMA, 2010, 17(6): 652-662. DOI: 10.1136/jamia.2009.002477.

26.Zhou X, Murugesan S, Bhullar H, et al. An evaluation of the THIN database in the OMOP Common Data Model for active drug safety surveillance[J]. Drug Saf, 2013, 36(2): 119-134. DOI: 10.1007/s40264-012-0009-3.

27.刘宏尉, 杨嘉永, 颜志文. 应用中国医院药物警戒系统对左氧氟沙星导致皮肤相关不良反应的主动监测研究 [J]. 海峡药学, 2022, 34(9): 135-138. [Liu HW, Yang  JY, Yan ZW. Automatic monitoring study of levofloxacin-induced skin-related ADRs based on the China Hospital Pharmacovigilance System[J]. Strait Pharmaceutical Journal, 2022, 34(9): 135-138.] DOI: 10.3969/j.issn.1006- 3765.2022.09.039.

28.郑轶. 基于中国医院药物警戒系统数据的药品不良反应主动监测探索[D]. 上海: 海军军医大学, 2023. DOI: 10.26998/d.cnki.gjuyu.2023.000310.

29.Choudhury O, Park Y, Salonidis T, et al. Predicting adverse drug reactions on distributed health data using federated learning[J]. AMIA Annu Symp Proc, 2019, 2019: 313-322.https://pubmed.ncbi.nlm.nih.gov/32308824/.

30.Tang Y, Yang J, Ang PS, et al. Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer[J]. Int J Med Inform, 2019, 128: 62-70. DOI: 10.1016/j.ijmedinf.2019.04.017.

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