Incorporating Learning Effects into Medical Device Active Safety Surveillance Methods

植入式医疗设备彻底改变了当代心血管护理， 用于广泛的急性和慢性心血管疾病。但医疗 器械设计错误或使用不当可能导致患者损伤的重大风险，并代表 这是美国一个重要的可预防的公共卫生风险。帮助识别与器械相关的 安全性问题，积极的、前瞻性的上市后安全性监测策略已被 由FDA推荐，并在方法学上进行评估。这种类型的监视提供了 与传统的不良事件报告策略相比具有显著优势。然而，所有这些 由于需要将学习效果纳入期望，方法面临挑战 关于安全。这些学习的影响一再被证明对 早期器械使用期间的结局。量化学习对成果的影响 与高风险心血管设备相关的研究将提高我们对内在 器械性能，从而确定使用此类器械进行最佳治疗的患者人群， 同时向设备制造商提供必要的反馈， 设备设计改进。另外，了解学习的影响可以确定 提供有针对性的培训机会，并帮助区分机构和运营商 可能加速实现最佳结果的特性， 特定心血管装置。 该提案旨在扩展先前验证的，开源的，积极的，前瞻性的 器械安全监督工具，通过开发和验证稳健的学习曲线（LC） 检测和量化算法，旨在同时考虑在 运营商和机构层面。我们提出了一个"盲目"的发展战略， 一个团队将生成具有LC影响的强大合成临床数据模拟器，另一个团队将生成具有LC影响的强大合成临床数据模拟器， 团队开发和应用LC检测和定量算法，而不了解 基础关系，通过顺序细化确定性能和准确性 和验证步骤。我们建议在真实数据中正式验证优化的LC工具 通过重新分析先前发表的LC对经导管瓣膜和血管的影响， 使用国家心血管登记系统的闭合器械。此外，LC工具将 纳入两项新型植入式植入物的主动、前瞻性器械安全性监测研究 使用大型临床注册中心的心血管器械。

Implantable medical devices have revolutionized contemporary cardiovascular care, and are used in a wide spectrum of acute and chronic cardiovascular conditions. However, medical device design fault or incorrect use may lead to significant risk of patient injury and represents an important preventable public health risk in the United States. To help identify device-related safety issues, a strategy of active, prospective, post-market safety surveillance has been recommended by the FDA, and evaluated methodologically. This type of surveillance offers significant advantages over traditional adverse event reporting strategies. However, all such approaches are challenged by the need to incorporate learning effects into expectations regarding safety. These learning impacts been repeatedly shown to have dramatic impacts on outcomes during early device experience. Quantifying learning effects on the outcomes associated with high-risk cardiovascular devices will improve our understanding of intrinsic device performance, thereby identifying patient populations best treated with such devices while simultaneously providing necessary feedback to device manufacturers to support iterative improvement in device design. Separately, understanding the impacts of learning may identify opportunities for targeted training as well as help to tease apart institutional and operator characteristics that may accelerate the achievement of optimal outcomes in the use of the specific cardiovascular device. This proposal seeks to extend the previously validated, open-source, active, prospective device safety surveillance tool, by developing and validating robust learning curve (LC) detection and quantification algorithms, designed to simultaneously account for the effects at the operator and institutional levels. We propose a “blinded” development strategy, in which one team will generate robust synthetic clinical data simulator with LC impacts, and the other team develops and applies LC detection and quantification algorithms, without knowledge of the underlying relationships, determine performance and accuracy through sequential refinement and validation steps. We propose to formally validate the optimized LC tools in real-world data through re-analysis of previously published LC effects on transcatheter valves and vascular closure devices using national cardiovascular registries. In addition, the LC tools will be incorporated into two active, prospective device safety surveillance studies of novel implantable cardiovascular devices using large clinical registries.