Document Type
Conference Paper
Date of this Version
7-12-2018
Publication Source
IEEE Engineering in Medicine and Biology Society. Conference Proceedings
Abstract
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 considerable 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 allows for the systematic incorporation of information available at the design of the trial. With this formulation, the CACT estimates the inappropriate therapy rate of Vitality 2 compared to Medtronic as 33.22% vs 15.62% (p
Keywords
computer-aided clinical trials, clinical trials, medical devices, trial simulation, physiological modeling
Recommended Citation
Kuk Jin Jang, James Weimer, Houssam Abbas, Zhihao Jiang, Jackson Liang, Sanjay Dixit, and Rahul Mangharam, "Computer Aided Clinical Trials for Implantable Cardiac Devices", IEEE Engineering in Medicine and Biology Society. Conference Proceedings . July 2018.
Previous Versions
Included in
Applied Statistics Commons, Clinical Trials Commons, Computer Engineering Commons, Electrical and Computer Engineering Commons, Investigative Techniques Commons
Date Posted: 15 July 2018
This document has been peer reviewed.