ArticlePDF Available

Computer Aided Clinical Trials for Implantable Cardiac Devices


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<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.
University of Pennsylvania
",(3$04,(5+4),++,+#?9:,49()4 #*/6636-5.05,,805.(5+7730,+#*0,5*,
Computer Aided Clinical Trials for Implantable
Cardiac Devices
Houssam Abbas
University of Pennsylvania/())(99,(9;7,55,+;
Zhihao Jiang
University of Pennsylvania@/0/(619,(9;7,55,+;
Kuk Jin Jang
University of Pennsylvania1(5.219,(9;7,55,+;
Marco Beccani
University of Pennsylvania),**(509,(9;7,55,+;
Jackson Liang
Hospital of University of Pennsylvania1(*296530(5.;7/9;7,55,+;
See next page for additional authors
6336=:/09(5+(++0:065(3=6829(: /H78,7690:68?;7,55,+;43()'7(7,89
!(8:6-:/, 647;:,85.05,,805.644659(5+:/, 3,*:80*(3(5+647;:,85.05,,805.
E097(7,809769:,+(:#*/63(83?644659 /H78,7690:68?;7,55,+;43()'7(7,89
68468,05-684(:06573,(9,*65:(*: 8,7690:68?76)6>;7,55,+;
Computer Aided Clinical Trials for Implantable Cardiac Devices
E0978,9,5:(:06509(<(03()3,(:#*/63(83?644659 /H78,7690:68?;7,55,+;43()'7(7,89
Computer Aided Clinical Trials for Implantable Cardiac Devices
Houssam Abbas, Zhihao Jiang, Kuk Jin Jang, Marco Beccani, Jackson Liang, Sanjay
Dixit and Rahul Mangharam
In this effort we investigate the design and use of physiological and device models to
conduct pre-clinical trials to provide early insight in the design and execution of the
actual clinical trial. Computer models of physiological phenomena like cardiac
electrical activity can be extremely complex. However, when the purpose of the
model is to interact with a medical device, then it becomes sufficient to model the
measurements that the device makes, e.g. the intra-cardiac electrograms (EGMs)
that an Implantable Cardioverter Defibrillator (ICD) measures. We present a
probabilistic generative model of EGMs, capable of generating exemplars of various
arrhythmias. The model uses deformable shape templates, or motifs, to capture the
variability in EGM shapes within one EGM channel, and a cycle length parameter to
capture the variability in cycle length in one EGM channel. The relation between
EGM channels, which is essential for determining whether the current arrhythmia is
potentially fatal, is captured by a time-delayed Markov chain, whose states model
the various combinations of (learned) motifs.
The heart model is minimally parameterized and is learned from real patient data.
Thus the statistics of key features reflect the statistics of a real cohort, but the model
can also generate rare cases and new combinations from the inferred probabilities.
On the device end, algorithms for signal sensing, detection and discrimination for
major ICD manufacturers have been implemented both in simulation and on
hardware platforms. The generated arrhythmia episodes are used as input to both
the modeled ICD algorithms and real ICDs as part of a Computer Aided Clinical Trial
In a CACT, a computer model simulates the inputs to the device (such as a new,
investigational ICD), and the device’s performance is evaluated. By incorporating
these results into the appropriate statistical framework, the Computer Aided Clinical
Trial results can serve as regulatory evidence when planning and executing an
actual clinical trial. We demonstrate this by conducting a mock trial similar to the
2005-2010 RIGHT trial which compared the discrimination algorithms from two
major ICD manufacturers. The results of the CACT clearly demonstrate that the
failed outcome of the RIGHT trial could have been predicted and provides statistical
support for deeper results that could have been captured prior to the trial.
... For this purpose, using mathematical models of physiological processes as virtual patients has recently received notable attention in the research community [1]. To name a few: the diabetes simulator introduced in [2] is used to develop and test artificial pancreas control algorithms; the model of hemodynamic responses to hemorrhage presented in [3] has been used in a hardware-in-the-loop setup to test fluid resuscitation algorithms [4]; a synthetic virtual cohort of heart electro-grams has been used in [5] to run computeraided clinical trials for implantable cardiac devices; and models of physiological responses to interacting drugs have been used in [6] to develop and test medication control algorithms. In addition, the U.S. potential for computer simulations to complement regulatory submissions for new medical devices [7]. ...
... A prevalent approach to conduct this sampling is to identify parameter values based on a dataset of subjects and use the identified models as virtual patients (e.g. as in [4], [11]). Alternatively, suitable physiological ranges for each parameter (calculated from the literature or based on the calibration of the model to a dataset) can be sampled to obtain potentially larger cohorts (e.g. as in [5], [12]). In some cases, additional information may be available about the distribution of each parameter and/or the relationship between the parameters, which can also be incorporated into the sampling procedure (e.g. as in [2], [13]). ...
The goal of target-controlled delivery of intravenous (IV) anesthetics is the achievement and maintenance of a suitable depth of hypnosis (DOH) in a fast and safe manner, where DOH is associated with a certain effect site (i.e. brain) drug concentration. Nowadays, the delivery of anesthetic drugs is performed by target-controlled infusion (TCI) pumps adjusting the delivery rate using an algorithm based on pharmacokinetic (PK) models having no feedback. However, the inaccuracy of concentration prediction using this PK model for certain individuals can be up to 100%. In this chapter, we show that the precision of anesthesia delivery can definitely be improved by realising a feedback loop with sensors able to provide measurements of the anesthetic concentration in body fluids in real time. We present two possible approaches for building the control feedback loop using plasma concentration measurements: one representing the classic method in pharmacokinetics based on Bayesian inference and another one being an example of classic method in control theory based on Kalman filter. The first one performs real-time re-estimation of PK model parameters with each new measurement, while the latter one estimates the offset values for drug concentration correction. The adjusted concentration values are further used to compute the personalised delivery rate using the classic TCI algorithm. To validate the algorithms’ robustness, we simulate measurements covering the maximum space of possible values using inter- and intra-patient variability of the statistical Eleveld’s (Eleveld, Proost, Cortinez, Absalom, Struys, Anesth Analg 118(6):1221–1237, 2014, [8]) PK model. This allows one to disturb the system to its extreme before testing it on patients. We provide the robustness analysis of these algorithms with respect to realistic measurement periods and delays.
Full-text available
The complexity of the healthcare ecosystem and the trans-disciplinary convergence which is essential for its function, makes it difficult to address healthcare as one domain. Data curation and analysis of the information may boost our health related knowledge. Increasing connectivity and improving infrastructure may help, among other things, to uncover facts and observations which may influence the future of global health.
ResearchGate has not been able to resolve any references for this publication.