Goswami Swarnali, Masurkar Prajakta P, Kaul Aashrey
Department of Pharmacy Administration, University of Mississippi, University, MS, USA.
Now with Neurocrine Biosciences, Inc San Diego, USA.
Expert Rev Med Devices. 2025 Jul;22(7):767-773. doi: 10.1080/17434440.2025.2512041. Epub 2025 May 28.
Despite the U.S. Food and Drug Administration (FDA)'s recognition of the value of real-world evidence (RWE) in the evaluation of medical devices, there is a lack of consensus among RWE stakeholders regarding the FDA's specific evidentiary requirements.
This paper reviews the publicly accessible FDA approval documents from January 2020 to July 2024 and describes the intended purpose of RWE incorporated into medical device approvals, i.e. to support claims of safety and/or effectiveness and its impact on the FDA's benefit-risk assessment.
Between January 2020 and July 2024, 117 medical devices included RWE in their submissions. Of these, 74 (63.25%) used RWE to support approval, while in 43 (36.75%) submissions, an RWE study was requested by the FDA. The most common submission types were 510(k) and Pre-Market Approval (PMA)-Panel Track (27.4%), followed by PMA-Original (21.9%) and De Novo (19.48%). RWE most frequently supported effectiveness (85.24%), safety (72.97%). Cardiovascular devices accounted for 44% of approvals incorporating RWE, with registry-based studies being the most common data source.
This review highlights key areas of FDA feedback on RWE studies, including concerns related to methodology and data quality emphasizing the need for careful selection of real-world data and rigorous study design.
尽管美国食品药品监督管理局(FDA)认可真实世界证据(RWE)在医疗器械评估中的价值,但RWE利益相关者对于FDA的具体证据要求尚未达成共识。
本文回顾了2020年1月至2024年7月可公开获取的FDA批准文件,并描述了纳入医疗器械批准的RWE的预期用途,即支持安全性和/或有效性声明及其对FDA获益-风险评估的影响。
在2020年1月至2024年7月期间,117种医疗器械在提交的材料中包含了RWE。其中,74种(63.25%)使用RWE来支持批准,而在43种(36.75%)提交材料中,FDA要求进行RWE研究。最常见的提交类型是510(k)和上市前批准(PMA)-专家小组跟踪(27.4%),其次是PMA-原始申请(21.9%)和全新申请(19.48%)。RWE最常支持有效性(85.24%)、安全性(72.97%)。纳入RWE的批准中,心血管器械占44%,基于注册的研究是最常见的数据源。
本综述突出了FDA对RWE研究反馈的关键领域,包括对方法和数据质量的担忧,强调了仔细选择真实世界数据和进行严谨研究设计的必要性。