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Computational Modeling & Regulatory Science (CM&RS) 3:

Virtual Patients in Clinical Trials

Wednesday, April 13, 10:30-12:00
Meridian Ballrooms 1, The Commons Hotel

Organizers: Marc Horner, Technical Lead, Healthcare, ANSYS, Inc.
Dawn Bardot, Vice President, Technology Innovation, Medical Device Innovation Consortium

"Incorporation of Stochastic Engineering Models as Prior Information in Bayesian Medical Device Trials"
Tarek Haddad, Sr. Manager, Modeling Integration and Statistics, Medtronic Cardiac Rhythm and Heart Failure

"Augmenting a Clinical Study with Virtual Patient Models: FDA and Industry Collaboration" DMD2016-8460*
Adam Himes, Sr. Principal Engineer, Mechanical Characterization and Analysis, Medtronic Cardiac Rhythm and Heart Failure
Marc Horner, Technical Lead, Healthcare, ANSYS, Inc.

"In Silico Imaging Methods within a Virtual Imaging Clinical Trial for Regulatory Evaluation"
Aldo Badano, Deputy Director (acting), Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/OMPT/FDA

"A Framework for Reconstructing 3D Rib Cage and Thoracic Volume in Spine Deformity Patients: An Innovative Simulation Software Development"
Charles Ledonio, Director of Spine Research, Department of Orthopaedic Surgery, University of Minnesota


Session Abstract:

Computer Modeling can provide evidence of device safety and performance, thus enabling quick and predictable access for patients to innovative technologies. A truly innovative example use of models is Virtual Patients to reduce the clinical trial burden for medical device trials. The first Virtual Patient example in this session utilizes computational models and data from engineering testing, predicate devices and international clinical trials and builds on the FDA guidance for the use of Bayesian Statistics in Medical Device Clinical Trials. The successful deployment of Virtual Patients for clinical trial design will result in reduced patient enrollment in clinical trials, enabling both cost and time savings and the acceleration of patient access in the to innovative therapies.


Session Organizer Bios:

Marc Horner, Technical Lead, Healthcare, ANSYS, Inc.
Dr. Marc Horner is the lead healthcare specialist at ANSYS Inc. Marc joined ANSYS after earning his PhD in Chemical Engineering from Northwestern University in 2001. Marc began by providing support and professional services for biomedical clients, primarily in the areas of cardiovascular devices, drug delivery, packaging, microfluidics and orthopedics. During this time, Marc developed numerous modeling approaches that can be used to establish the efficacy and safety of medical devices. Marc now helps coordinate business and technology development for the health care sector in North America.

Dawn Bardot, Vice President, Technology Innovation, Medical Device Innovation Consortium
With more than 15 years of experience in computational model validation and uncertainty quantification, Dawn Bardot, Ph.D, brings a wealth of experience to her role as Vice President of Technology Innovation at the Medical Device Innovation Consortium (MDIC). She is passionate about the application of modeling and simulation to improve health care and lower the cost of bringing products to market. She is an active member of the American Society of Mechanical Engineers (ASME); serves as Chair of the Verification and Validation in Computation Fluid Dynamics and Heat Transfer Committee; and is Sub-Group Chair of the Committee on Verification and Validation in Computational Methods for Medical Devices. Dr. Bardot has a BS and MS in mechanical engineering from Kansas State University, a PhD in mechanical engineering from the University of Washington, was an Innovation Fellow at the University of Minnesota Medical Devices Center, and spent two summers as a Faculty Fellow at NASA Marshal Space Flight Center.


Speaker Bios:

Tarek Haddad, Sr. Manager, Modeling Integration and Statistics, Medtronic Cardiac Rhythm and Heart Failure
Tarek Haddad is Sr. Manager, Modeling Integration and Statistics at Medtronic within Cardiac Rhythm and Heart Failure. His focus is on developing computational and stochastic models for predictive product reliability, efficacy, and clinical research. He specializes in Bayesian modeling and has numerous publications in these areas. Tarek attended the University of Minnesota, receiving a BA in Mathematics and a MS in Biostatistics.

Adam Himes, Sr. Principal Engineer, Mechanical Characterization and Analysis, Medtronic Cardiac Rhythm and Heart Failure
Adam Himes is a mechanical engineer working in the Cardiac Rhythm and Heart Failure division of Medtronic. He spends most of his time working on improved ways to predict mechanical reliability of implanted systems. Prior to joining Medtronic in 2009, Adam worked at Seagate Technology on mechanical reliability of hard disc drives. He has BS and MS degrees in Mechanical Engineering from Michigan Tech and the University of Minnesota.

Aldo Badano, Deputy Director (acting), Division of Imaging, Diagnostics, and Software Reliability, OSEL/CDRH/OMPT/FDA
Aldo Badano is a Senior Biomedical Researcher and the Laboratory Leader for Imaging Physics in the Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration. Dr. Badano leads a program on the characterization, modeling and assessment of medical image acquisition and display devices using experimental and computational methods. Dr. Badano is also an affiliate faculty of Bioengineering at the University of Maryland, College Park and at the Computer Science and Electrical Engineering Department of University of Maryland, Baltimore County. He received a PhD degree in Nuclear Engineering and a MEng in Radiological Health Engineering from the University of Michigan in 1999 and 1995, and a ChemEng degree from the Universidad de la República, Montevideo, Uruguay in 1992. He serves as Associate Editor for several scientific journals and as a reviewer of technical proposals for DOD and NIH. Dr. Badano has authored more than 250 publications and a tutorial textbook on medical displays.

Charles Ledonio, Director of Spine Research, Department of Orthopaedic Surgery, University of Minnesota
Dr. Ledonio is an Orthopaedic surgeon from the Philippines. He is Director of spine research at the Department of Orthopaedic Surgery, University of Minnesota. He has received honors for his outstanding performance as an intern from his education in the Philippines. He completed his Orthopaedic Surgery residency under the chairmanship of Dr. Ramon Gustilo.


Presentation Abstracts:

"Incorporation of Stochastic Engineering Models as Prior Information in Bayesian Medical Device Trials"
Modern implantable medical devices have brought improved quality of life to many patients. Evaluation via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. Incorporation of these models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I and type II error rates. This talk presents a straightforward method for augmenting a clinical trial using virtual patient data, where the number of virtual patients is based on the similarity between modeled and observed data. The use of this method is illustrated by a case study based on a model for cardiac lead fracture.

"Augmenting a Clinical Study with Virtual Patient Models: FDA and Industry Collaboration" DMD2016-8460*
Medical device manufacturers are increasingly using predictive computer models during the product development process. These models are created for a specific context of use in order to simulate the device and relevant anatomy and physiology associated with predicting safety and efficacy outcomes. Since these models are created to predict the same endpoints that would be observed in clinical practice with the same population variability, we use the term virtual patient models to refer to these simulations. These virtual patient models can be incorporated into a study in way that is analogous to how some Bayesian clinical trials incorporate historical data as prior information.

To properly inform clinical evaluation, the virtual patient model must: (1) Simulate the clinical outcome of interest, (2) Represent patient-to-patient variability, (3) Use confidence intervals in the model output to represent uncertainty due to input sample size, model bias, gage R&R, etc.

The construction of a virtual patient model will be different for each particular outcome, and may involve a variety of disciplines. Applications that lend themselves to virtual patient models will involve local mechanisms that are well understood. Potential examples include closed loop glucose control, heart valve frame or stent fracture, orthopedic implant wear or fracture, or cardiac lead fracture.

"In Silico Imaging Methods within a Virtual Imaging Clinical Trial for Regulatory Evaluation"
The field of in silico methods for analyzing the performance of novel x-ray breast imaging products has made significant progress over the past 15 years. We will describe state-of-the-art computational techniques for obtaining evidence consistent with the results of resource-intensive clinical trials using humans (patients and clinicians) for the purpose of regulatory evaluation. We will describe a demonstration in silico version of a reader study in a previous submission to the Agency, including random realistic breast models, models of the physics of image generation and reconstruction, and models for image interpretation. All models used in this project will be made freely available as open-source software. This demonstration project is expected to affect how Sponsors generate regulatory evidence for breast imaging products by reducing the need for clinical testing on patients.

"A Framework for Reconstructing 3D Rib Cage and Thoracic Volume in Spine Deformity Patients: An Innovative Simulation Software Development"
Assessment for spine-deformity patients currently still relies on 2D plain radiographs. However, pathological spine, such as scoliosis and kyphosis, is truly a three-dimensional problem, so the ideal imaging should be shown in 3D [1]. In the past decades, various approaches related to 3D reconstruction of the spine have been proposed [2-4], but very few of them have been applied to clinical use [5]. Thoracic rib cage deformity is a combined disorder between the ribs and vertebrae. The progression of the rib cage deformity may trigger cardiopulmonary damage to the patients because the smaller thoracic volume restricts the expansion space of the lungs. Traditionally, the pulmonary function test (PFT) is used to evaluate the function of the lungs. However, PFTs are not routinely utilized in some cases, especially in very young patients (normally prior to 10 years of age). Thoracic volume is not yet easily quantified in a patient specific fashion. Clinically, the computed tomography (CT) scan is the most common way for doctors to measure and examine the thoracic space in spine disorder patients [6]. However, CT-scan usage is limited because of the high-dose radiation and significantly increased risk of cancer for pediatric spine-deformity patients, in particular [7]. As a result, it is necessary to find alternatives to measure the volume of the thoracic cavity that can benefit spine patients. To overcome the abovementioned limitations, we present a new approach to reconstruct the thoracic rib cage and thoracic volume in 3D via two orthogonal radiographs.


*Presentation based on 2016 DMD Conference Call for Papers accepted paper submissions. Final two-page technical brief will be published in the September 2016 Issue ASME Journal of Medical Devices.


Related Sessions:

CM&RS 1: An Intro to FDA Medical Device Development Tools (MDDT) and Updates from Submitters
CM&RS 2: Advances in Heart Modeling and Cardiovascular Interventions
CM&RS 4: Verification and Validation: A Pathway to Establish Credible Computer Models

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