中国药物警戒 ›› 2023, Vol. 20 ›› Issue (2): 236-240.
DOI: 10.19803/j.1672-8629.20220562

• 综述 • 上一篇    

医疗器械上市后安全性数据来源和信号识别方法研究进展

赵燕1, 李栋, 程音婕2, 江滨3, 赵一飞1, 董放1#, 袁静2*   

  1. 1国家药品监督管理局药品评价中心,国家药品监督管理局药物警戒研究与评价重点实验室,北京 100022;
    2复旦大学药学院,上海 201203;
    3北京大学药学院,北京 100191
  • 收稿日期:2022-09-22 出版日期:2023-02-15 发布日期:2023-02-17
  • 通讯作者: *袁静,女,博士,副教授,药物流行病学及真实世界研究。E-mail: jyuan@fudan.edu.cn;#为共同通信作者。
  • 作者简介:赵燕,女,硕士,高级工程师,医疗器械不良事件监测与评价。Δ为并列第一作者。
  • 基金资助:
    国家重点研发计划:中国盆底手术移植物并发症登记及盆底康复预防平台体系建立(2021YFC2701300)、盆底重建手术移植物并发症的登记研究及评价标准化与质量管理(2021YFC2701302); 中国药品监管科学行动计划第二批重点项目([2021]37-10)

Advances in studies on post-market safety data sources and signal detection for medical devices

ZHAO Yan1, LI Dong, CHENG Yinjie2, JIANG Bin3, ZHAO Yifei1, DONG Fang1#, YUAN Jing2*   

  1. 1Center for Drug Reevaluation, NMPA/NMPA Key Laboratory for Research and Evaluation of Pharmacovigilance, Beijing 100022, China;
    2School of Pharmacy, Fudan University, Shanghai 201203, China;
    3School of Pharmacy, Peking University, Beijing 100191, China
  • Received:2022-09-22 Online:2023-02-15 Published:2023-02-17

摘要: 目的 综述医疗器械上市后安全性数据来源和信号识别研究的进展,为上市后医疗器械安全性监管提供重要循证依据。方法 检索国内外医疗器械上市后安全性信号识别的文献,梳理医疗器械上市后安全性数据来源和安全性信号识别方法的最新进展。结果与结论 近年来随着信息技术的高速发展,医保赔付数据、电子病历、登记数据等真实世界数据已成为医疗器械上市后安全性信号识别的重要数据来源。随着医疗器械上市后安全性数据源的不断扩大,多种信号识别方法已逐步应用于医疗器械上市后安全性监测研究,社交媒体数据和器械使用者自身收集数据等也成为新兴数据来源。医疗器械安全性信号识别常综合运用多种方法,如传统的比值失衡测量法、药物流行病学方法、机器学习方法等。为克服观察性研究中偏倚问题,很多研究也同时运用倾向评分法,以更准确客观地评估器械产品的安全性。

关键词: 医疗器械, 主动监测, 上市后研究, 信号识别, 监管科学, 安全性

Abstract: Objective To summarize the recent research advances in post-market safety data sources and signal detection for medical devices so as to provide evidence for post-market regulation of medical devices. Methods Literature on post-market safety signal detection for medical devices was searched to keep track of the latest progress in post-market safety data sources and signal detection methods. Results and Conclusion With the rapid development of information technology, such real-world data as medical insurance claims, electronic health records, and registry data have become the main sources of data on post-market safety signal detection for medical devices. With the expansion of sources of data, multiple signal detection approaches have been developed and applied in post-market safety surveillance for medical devices. Social media data and data automatically collected by medical devices are also emerging sources of data. In the course of signal detection for medical devices, a variety of methods are used, including disproportionality methods, pharmacoepidemiology approaches, and machine learning methods. To reduce selection bias in observational studies, propensity scores are often applied in pharmacoepidemiology studies in order to evaluate the safety of medical devices more effectively.

Key words: medical device, active surveillance, post-market surveillance, signal detection, regulatory science, safety

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