Fracture prediction of cardiac lead medical devices using Bayesian networks

Haddad, Tarek; Himes, Adam; Campbell, Michael

doi:10.1016/j.ress.2013.11.005

Cited by 28 publications

(26 citation statements)

References 52 publications

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“…This is completely analogous to reducing sampling error and therefore the size N 0 should be commensurate with the clinical application, for example frequency of an event rate. Examples of virtual patient outcomes include blood glucose level control in diabetes therapy (Kovatchev, et al, 2009) or ICD lead fracture (Haddad, et al, 2014).…”

Section: Virtual Patient Model Overviewmentioning

confidence: 99%

“…This assumption is similar to standard statistical practice where a standard distribution (e.g., normal) is selected because it fits the data reasonably well. Data from a well-developed engineering model will frequently be highly similar to clinical data (see, e.g., Haddad, et al, 2014).…”

Section: Connection Between the Engineering Model And The Clinical Modelmentioning

confidence: 99%

“…As an example of a probabilistic engineering model, we consider the case of cardiac lead fracture rate inside the heart, similar to Haddad et al (2014). The heart contracts with every heartbeat, causing the lead to bend.…”

Section: Scenario For a Virtual Patient Engineering Modelmentioning

confidence: 99%

“…With recent advances in computational modeling it is now possible to simulate device outcomes based on engineering principles (Haddad, et al, 2014;Wilkoff, et al, 2013;Kovatchev, et al, 2009). However, there are few published examples of how these methods might positively affect the process of clinical evaluation (FDA, 2011;Mulugeta, et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Haddad

Himes

Thompson

et al. 2017

Journal of Biopharmaceutical Statistics

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Evaluation of medical devices 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. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering 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 error rate and power.

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Section: Virtual Patient Model Overviewmentioning

confidence: 99%

Section: Connection Between the Engineering Model And The Clinical Modelmentioning

confidence: 99%

Section: Scenario For a Virtual Patient Engineering Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Haddad

Himes

Thompson

et al. 2017

Journal of Biopharmaceutical Statistics

Self Cite

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show abstract

“…Computational modeling and simulation (CM&S) are anticipated to play an increasingly significant role in the medical device industry in the coming years [1]. Emerging applications of CM&S include: supporting claims of substantial equivalence or safety and effectiveness of a medical device in regulatory submissions, augmenting clinical trial data with evidence provided by "virtual clinical trials" [2][3][4], and providing CM&S-derived diagnostic information for clinical decision support (e.g., [5,6]). Such uses of CM&S should be accompanied with evidence of model credibility that is commensurate with the risk associated with the intended context of use (COU) of the model [7].…”

Section: Introductionmentioning

confidence: 99%

Method of manufactured solutions code verification of elastostatic solid mechanics problems in a commercial finite element solver

Aycock¹,

Rebelo²,

Craven³

2020

Computers & Structures

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Much progress has been made in advancing and standardizing verification, validation, and uncertainty quantification practices for computational modeling in recent decades. However, examples of rigorous code verification for solid mechanics problems in the literature remain scarce, particularly for commercial software and for the non-trivial large-deformation analyses and nonlinear materials typically needed to simulate medical devices. Here, we apply the method of manufactured solutions (MMS) to verify a commercial finite element code for elastostatic solid mechanics analyses using linear-elastic, hyperelastic (neo-Hookean), and quasi-hyperelastic (Hencky) constitutive models. Analytical source terms are generated using either Python/SymPy or Mathematica and are implemented in ABAQUS/Standard without modification to the solver source code. Source terms for the three constitutive models are found to vary nearly six orders of magnitude in the number of mathematical operations they contain. Refinement studies reveal second-order displacement convergence in response to mesh refinement for all constitutive models and first-order displacement convergence in response to increment refinement for the finite-strain problems. We also investigate the sensitivity of MMS convergence order to minor coding errors using an exploratory case. Code used to generate the MMS source terms and the input files for the simulations are provided as supplemental material.

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Capability and Design for Reliability

2019

Practical Applications of Bayesian Reliability

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Fracture prediction of cardiac lead medical devices using Bayesian networks

Cited by 28 publications

References 52 publications

Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Method of manufactured solutions code verification of elastostatic solid mechanics problems in a commercial finite element solver

Capability and Design for Reliability

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