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Applying a Computational Model Credibility Framework to Physiological Closed-loop Controlled Medical Device Testing.

Christopher G Scully1, Pras Pathmanathan1, Chathuri Daluwatte1

  • 1Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD.

IEEE Life Sciences Conference. Life Sciences Conference
|September 13, 2021
PubMed
Summary
This summary is machine-generated.

Computational models are crucial for testing safety-critical physiological closed-loop controlled medical devices. A risk-informed framework enhances the credibility of these models, ensuring reliable pre-clinical evidence for device evaluation.

Keywords:
computational modelsphysiological closed-loop controlled systemsstandards

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Area of Science:

  • Biomedical Engineering
  • Medical Device Technology
  • Computational Modeling

Background:

  • Physiological closed-loop controlled medical devices integrate patient monitoring and therapy delivery for automated treatment titration.
  • Computational models of physiological systems offer a method for pre-clinical testing of these safety-critical devices.
  • The reliability of model-based testing hinges on the credibility of the computational models employed.

Purpose of the Study:

  • To examine the application of a novel risk-informed framework for establishing computational model credibility.
  • To evaluate the suitability of this framework for physiological closed-loop controlled devices.
  • To enhance the confidence in pre-clinical evidence generated by computational models for medical devices.

Main Methods:

  • Review and application of a recently developed risk-informed framework for computational model credibility.
  • Analysis of the framework's components in the context of physiological closed-loop controlled systems.
  • Assessment of how the framework addresses the unique challenges of modeling dynamic physiological interactions.

Main Results:

  • The risk-informed framework provides a structured approach to evaluating computational models for medical devices.
  • The framework's principles are adaptable to the complexities of physiological closed-loop controlled systems.
  • Applying the framework can systematically improve the credibility of models used in device evaluation.

Conclusions:

  • A risk-informed framework is a valuable tool for assessing the credibility of computational models in medical device applications.
  • This approach can bolster the evidence base for the safety and performance of physiological closed-loop controlled devices.
  • Enhanced model credibility leads to greater confidence in pre-clinical testing and regulatory acceptance.