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Computer Aided Clinical Trials for Implantaule Cardiac Devices

机译:植入心脏装置的计算机辅助临床试验

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In this paper we aim to answer the question, “How can modeling and simulation of physiological systems be used to evaluate life-critical implantable medical devices?” Clinical trials for medical devices are becoming increasingly inefficient as they take several years to conduct, at very high cost and suffer from high rates of failure. For example, the Rhythm ID Goes Head-to-head Trial (RIGHT) sought to evaluate the performance of two arrhythmia discriminator algorithms for implantable cardioverter defibrillators, Vitality 2 vs. Medtronic, in terms of time-to-first inappropriate therapy, but concluded with results contrary to the initial hypothesis - after 5 years, 2,000+ patients and at considerble ethical and monetary cost. In this paper, we describe the design and performance of a Computer-aided Clinical Trial (CACT) for Implantable Cardiac Devices where previous trial information, real patient data and closed-loop device models are effectively used to evaluate the trial with high confidence. We formulate the CACT in the context of RIGHT using a Bayesian statistical framework. We define a hierarchical model of the virtual cohort generated from a physiological model which captures the uncertainty in the parameters and allow for the systematic incorporation of information available at the design of the trial. With this formulation, the estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% (p<;0.001), which is comparable to the original trial. Finally, we relate the outcomes of the computer-aided clinical trial to the primary endpoint of RIGHT.
机译:在本文中,我们的目标是回答这个问题,“如何建模和生理系统的仿真被用来评估生命攸关的植入式医疗设备?”因为他们需要数年进行,成本非常高,从失败率很高遭受的医疗器械临床试验正变得越来越低效。例如,节奏ID去头 - 头试验(RIGHT)试图评估两种心律失常鉴别算法可植入心律转复除颤,活力2对比美敦力的性能,在时间 - 第一不恰当治疗方面,但结论与违背了最初的假设结果 - 5年后,2000名+和患者在不少时候道德和金钱成本。在本文中,我们描述了植入式心脏设备,其中前面的审判信息,真正的病人数据和闭环装置模型有效地用来评价高可信度试验计算机辅助临床试验(CACT)的设计和性能。我们制定使用贝叶斯统计框架中右的背景下,CACT。我们可以确定,从捕捉参数中的不确定性和允许的可在试验的设计信息系统地纳入一个生理模型生成的虚拟队列的层次模型。与此制剂中,估计相比给Medtronic活力2的不适当的治疗率33.22%和15.62%(P <0.001),这与原来的试验。最后,我们与计算机辅助临床试验的结果向右的主要终点。

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