An Automated System to Monitor Medical Device Safety

描述(由申请人提供)： 医疗器械上市后的安全监测是一项复杂的任务，加上新医疗技术的迅速传播、数据收集缺乏标准以及被动不良事件报告机制不足。建立有效的监测系统具有挑战性，因为数据通常不能在可接受的时间范围内获得，而且对于用来确定低频安全威胁的最适当的方法缺乏共识。我们开发了一个计算机化的工具，Delta(数据提取和纵向时间分析系统)，它可以通过使用各种统计监测工具对临床结果数据库进行持续监测，来监测新医疗设备的不良事件发生率。我们在介入心脏病学领域内的一个单一中心的大型临床数据库上测试和验证了Delta，并表明该系统在识别极低频率事件方面是有效的。此外，我们还探索了由事件趋势触发的各种警报算法。 我们建议扩展Delta监测系统，以监测马萨诸塞州范围内的介入心脏病学强制结果数据登记，该登记是根据国家标准严格收集的。将对Delta系统进行改进，以支持利用二分法和连续成果分析方法进行持续监测。此外，将对照登记册的历史数据以及发现重大安全问题的随机试验数据对该系统进行验证。此外，我们建议在MA的四个参与中心将Delta实施为分析引擎的安全分布式网络。我们将使用量化已披露数据的“匿名性”程度的算法来“识别”患者信息。该系统将通过评估几类新设备的安全性进行评估，并与传统的不良事件检测方法进行比较，这些新设备包括新药洗脱冠状动脉支架、血栓保护设备和血管闭合设备，涉及4万多名患者。将对Delta网络的敏感性、特异性、节省时间和成本效益进行前瞻性评估。 Delta网络可能为现有的医疗器械安全监测方法提供一种有价值的补充方法。随着结果数据库的出现，这种方法可以很容易地扩展到监测介入心脏病学以外的技术的安全性。

DESCRIPTION (provided by applicant): Post-market safety surveillance of medical devices is a complex task compounded by rapid dissemination of new medical technology, lack of standards in data collection, and inadequate passive adverse event reporting mechanisms. Building an effective surveillance system is challenging because data are generally not available in an acceptable timeframe and there is lack of consensus regarding the most appropriate methodologies to be used to identify low frequency safety threats. We have developed a computerized tool, DELTA (Data Extraction and Longitudinal Time Analysis system), that can monitor the adverse event rates of new medical devices through the continuous surveillance of clinical outcomes databases using a variety of statistical monitoring tools. We tested and validated DELTA on a large clinical database at a single center within the domain of interventional cardiology and showed that the system was efficient in identifying very low frequency events. In addition we have explored various alerting algorithms triggered by event trends. We propose to extend the DELTA surveillance system to monitor a Massachusetts state-wide mandated outcomes data registry in interventional cardiology that is rigorously collected according to national standards. The DELTA system will be modified to support continuous monitoring utilizing dichotomous and continuous outcome analytic methods. In addition, the system will be validated against historical registry data as well as randomized trial data in which there were significant safety issues identified. Also, we propose to implement DELTA as a secure distributed network of analytic engines at four participating centers in MA. We will "de-identify" patient information using algorithms that quantify the degree of "anonymity" of the disclosed data. This system will be evaluated and compared with traditional methods for adverse event detection by assessing the safety of several classes of new devices, including new drug eluting coronary stents, embolic protection devices, and vascular closure devices in over 40,000 patients. The sensitivity, specificity, time savings and cost effectiveness of the DELTA network will be prospectively evaluated. The DELTA network may offer a valuable complementary approach to existing methods for medical device safety surveillance. This approach can be readily extended to monitor the safety of technologies outside of interventional cardiology as outcomes data repositories become available.