Incorporating Learning Effects into Medical Device Active Safety Surveillance Methods

植入式医疗设备已经彻底改变了当代的心血管护理， 广泛用于各种急性和慢性心血管疾病。然而，医学上 设备设计错误或使用不当可能会导致患者受伤的重大风险，并代表 在美国，这是一个重要的可预防的公共卫生风险。帮助识别与设备相关的 安全问题，一项积极的、前瞻性的、上市后安全监测的战略 由FDA推荐，并进行方法学评估。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 需要将学习效果纳入期望，这对方法提出了挑战 关于安全问题。这些学习的影响反复被证明对 早期设备体验期间的结果。量化学习对结果的影响 与高危心血管设备相关将提高我们对内在 设备性能，从而确定使用此类设备进行最佳治疗的患者群体 同时向设备制造商提供必要的反馈，以支持迭代 改进了设备设计。另外，了解学习的影响可能会识别 提供有针对性的培训机会，并帮助区分机构和经营者 可加速取得最佳结果的特点 特定的心血管设备。 这项提议旨在扩展以前经过验证的、开源的、活跃的、预期的 设备安全监控工具，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 运营者和机构层面。我们提出了一个“盲目”的发展战略， 一个团队将生成具有LC Impact功能的健壮的合成临床数据模拟器，而另一个团队将生成 团队开发和应用LC检测和量化算法，而不知道 底层关系，通过顺序细化确定性能和准确性 和验证步骤。我们建议在实际数据中对优化后的LC工具进行正式验证 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处的封堵器。此外，LC工具将是 纳入新型植入物的两项积极的、前瞻性的设备安全监测研究 使用大型临床登记的心血管设备。

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.