A System to Monitor Safety in Interventional Cardiology

描述(申请人提供)：介入心脏病学是一个动态发展的医学领域，代表了美国用于治疗冠状动脉疾病最频繁的血运重建策略。在过去的几年里，许多新的医疗设备被引入，这极大地扩大了可以成功治疗的患者的范围。虽然新设备的发布审批受到严格监管，但上市后的安全评估却没有那么明确的定义。目前评估医疗器械安全性的做法依赖于提供者的自愿报告，并且存在报告不足、时间延迟和无法评估不良事件发生率的问题。我们建议开发一个系统，以监测引入临床介入心脏病学实践的新医疗设备的安全性，利用贝叶斯更新策略来解决其中一些缺陷。 贝叶斯方法将现有的关于医疗设备安全性的知识，通常来自已公布的临床试验数据，以及观察到的数据纳入到不良事件风险的估计中。使用经过验证的风险分层方法，我们建议监测不良事件的频率，以便评估随着时间的推移手术风险的当前状态。我们计划根据历史数据和前瞻性数据对该方法进行评估，探讨在介入心脏病学中使用两种新兴设备类别的风险：药物洗脱支架和滤过型远端保护设备。对309名接受旋转性动脉粥样硬化切除术的患者的方法学进行的初步探索表明，在研究期间监测风险评估的演变是可行的。 我们建议建立一个正式的理论框架，用于建立先验概率估计，以及比较贝叶斯系统和经典统计结果的方法。然后，我们将开发安全监测分析软件，实施贝叶斯方法和经典统计方法，并将该系统部署为我们设施中现有结果跟踪数据库系统的一个组成部分。我们提出了一个详细的分析，这将有助于确定贝叶斯框架在介入心脏病学中监测设备安全性方面是否比经典方法有任何优势。一旦得到验证，分析软件将公开可用。

DESCRIPTION (provided by applicant): Interventional cardiology is a dynamically evolving field of medicine and represents the most frequent revascularization strategy used for the treatment of coronary artery disease in the United States. Over the past several years, numerous new medical devices have been introduced, which have greatly expanded the spectrum of patients that can be successfully treated. While the approval for the release of new devices is rigorously regulated, the post-marketing evaluation of safety is less clearly defined. Current practices for assessing the safety of medical devices depend on voluntary reporting by providers, and suffer from underreporting, temporal delays and the inability to assess rates of adverse events. We propose to develop a system to monitor the safety of new medical devices introduced into clinical interventional cardiology practice, utilizing a Bayesian updating strategy that addresses some of these deficiencies. The Bayesian methodology incorporates existing knowledge regarding the safety of a medical device, typically from published clinical trial data, as well as observed data into the estimate of the risk of an adverse event. Using validated risk stratification methods, we propose to monitor the frequency of adverse events in order to assess the current state of risk of a procedure over time. We plan to evaluate the methodology on both historical and prospective data exploring the risks associated with the use of two emerging device classes within interventional cardiology: drug eluting stents, and filter-type distal protection devices. Preliminary exploration of the methodology for 309 patients who underwent rotational atherectomy demonstrates the feasibility of monitoring the evolution of risk estimates over the study period. We propose to develop a formal theoretic framework for the establishment of prior probability estimates as well as methods for comparing results from the Bayesian system to that of classical statistics. We will then develop the safety monitoring analytic software, implementing both the Bayesian methodology as well as classical statistical methods, and deploy the system as a component of the outcomes tracking database system in existence in our facility. We propose a detailed analysis that will help determine if the Bayesian framework has any advantages over classical methods in monitoring device safety in interventional cardiology. Once validated, the analytic software will be made publicly available.