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Title: Applicability Analysis of Validation Evidence for Biomedical Computational Models

Abstract

Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibility of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The frameworkmore » can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.« less

Authors:
 [1];  [2];  [3];  [2]
  1. Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
  2. Food and Drug Administration (FDA), Silver Spring, MD (United States). Office of Science and Engineering Laboratories (OSEL). Center for Devices and Radiological Health (CDRH)
  3. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Food and Drug Administration (FDA), Silver Spring, MD (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE; Food and Drug Administration (FDA) (United States)
OSTI Identifier:
1399491
Report Number(s):
SAND2017-2092J
Journal ID: ISSN 2377-2158; 651131
Grant/Contract Number:
AC04-94AL85000
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Verification, Validation and Uncertainty Quantification
Additional Journal Information:
Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2377-2158
Publisher:
ASME
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; computer simulation; simulation; stents; biomedicine

Citation Formats

Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., and Morrison, Tina M.. Applicability Analysis of Validation Evidence for Biomedical Computational Models. United States: N. p., 2017. Web. doi:10.1115/1.4037671.
Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., & Morrison, Tina M.. Applicability Analysis of Validation Evidence for Biomedical Computational Models. United States. doi:10.1115/1.4037671.
Pathmanathan, Pras, Gray, Richard A., Romero, Vicente J., and Morrison, Tina M.. 2017. "Applicability Analysis of Validation Evidence for Biomedical Computational Models". United States. doi:10.1115/1.4037671.
@article{osti_1399491,
title = {Applicability Analysis of Validation Evidence for Biomedical Computational Models},
author = {Pathmanathan, Pras and Gray, Richard A. and Romero, Vicente J. and Morrison, Tina M.},
abstractNote = {Computational modeling has the potential to revolutionize medicine the way it transformed engineering. However, despite decades of work, there has only been limited progress to successfully translate modeling research to patient care. One major difficulty which often occurs with biomedical computational models is an inability to perform validation in a setting that closely resembles how the model will be used. For example, for a biomedical model that makes in vivo clinically relevant predictions, direct validation of predictions may be impossible for ethical, technological, or financial reasons. Unavoidable limitations inherent to the validation process lead to challenges in evaluating the credibility of biomedical model predictions. Therefore, when evaluating biomedical models, it is critical to rigorously assess applicability, that is, the relevance of the computational model, and its validation evidence to the proposed context of use (COU). However, there are no well-established methods for assessing applicability. In this paper, we present a novel framework for performing applicability analysis and demonstrate its use with a medical device computational model. The framework provides a systematic, step-by-step method for breaking down the broad question of applicability into a series of focused questions, which may be addressed using supporting evidence and subject matter expertise. The framework can be used for model justification, model assessment, and validation planning. While motivated by biomedical models, it is relevant to a broad range of disciplines and underlying physics. Finally, the proposed applicability framework could help overcome some of the barriers inherent to validation of, and aid clinical implementation of, biomedical models.},
doi = {10.1115/1.4037671},
journal = {Journal of Verification, Validation and Uncertainty Quantification},
number = 2,
volume = 2,
place = {United States},
year = 2017,
month = 9
}

Journal Article:
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  • Computational fluid dynamic (CFD) models of the respiratory system provide a quantitative, biological basis for extrapolating the localized dosimetry of inhaled materials and improving human health risk assessments based upon inhalation studies conducted in animals. Nevertheless, model development and validation have historically been tedious and time-consuming tasks that have traditionally limited CFD’s wider utilization for inhalation research. In recognition of this we previously reported on the use of proton (1H) Magnetic Resonance (MR) imaging for visualizing nasal-sinus passages in the rat, and on the use of three-dimensional (3D) image data for speeding computational mesh generation. Here, detailed 3D 1H MRmore » imaging of pulmonary casts is reported, mesh generation is described in more detail, simulated gas-flows in nasal-sinus airways are presented, and the feasibility of validating CFD predictions with MR is tested by imaging the dynamics of hyperpolarized 3He at physiological flow rates in a straight pipe with a diameter comparable to the rat trachea. Results show that measured laminar flow structure is significantly blurred by rapid 3He diffusion but that the degree of blurring is generally predictable from the diffusion equation. Findings therefore support the notion that MR imaging is not only useful for defining airway architecture but also rapid CFD validation, and in this context, progress towards applications involving live animals and airway models is described.« less
  • To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less
  • The paper discusses the applicability of a one-dimensional approximation in a recently proposed model of ablation of carbon by a nanosecond laser pulse that considers the kinetics of the process. The model approximates the process as sublimation and combines conduction heat transfer in the target with the gas dynamics of the ablated plume which are coupled through the boundary conditions at the interface. The ablated mass flux and the temperature of the ablating material are obtained from the conservation relations at the interface derived from the moment solution of the Boltzmann equation for arbitrarily strong evaporation. It is shown thatmore » in the one-dimensional approximation the surface pressure and the ablation rate are too low and that the ablation rate is restricted most of the time by the kinetic theory limitation on the maximum mass flux that can be attained in a phase-change process. As a consequence, the model overpredicts the surface temperature and the duration of the process. However, it predicts the total ablated mass with good accuracy.« less