Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical
Ramin Bighamian1, Jin-Oh Hahn2, George Kramer3
1Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, United States of America.
Plos One
|April 30, 2021
Summary
This study introduces improved methods for evaluating physiological models used in closed-loop medical devices. A refined blood volume model demonstrated superior calibration and predictive accuracy, enhancing PCLC device safety assessments.
Area of Science:
- Biomedical Engineering
- Physiological Modeling
- Medical Device Development
Background:
- Physiological closed-loop controlled (PCLC) medical devices require rigorous safety and efficacy testing.
- Mathematical models are crucial for PCLC device design and evaluation, but their fidelity and performance measures need validation.
- Uncertainties in model accuracy and performance metrics hinder reliable PCLC device evaluation.
Purpose of the Study:
- To develop tools for assessing physiological model accuracy and establish fundamental measures for predictive capability.
- To refine a physiological blood volume (BV) model and compare its performance against an original model.
- To enhance the credibility assessment and selection process for physiological models in PCLC device evaluation.
Main Methods:
- Developed and applied novel tools for evaluating physiological model accuracy and predictive capability.
- Constructed a refined physiological blood volume model, expanding on a previously developed version.
- Utilized experimental data from 16 sheep undergoing hemorrhage and fluid resuscitation for model comparison.
Main Results:
- The refined BV model showed significant improvements in calibration performance (RMSE: 9%, P=0.03; multi-dimensional measure: 48%, P=0.02) compared to the original model.
- A comparable Akaike Information Criterion (AIC) value confirmed that the refined model's enhanced performance was not due to data over-fitting.
- The refined model demonstrated superior physiological predictive capability in subject-specific steady-state, transient responses, and leave-one-out inter-subject scenarios (P < 0.02).
Conclusions:
- The refined physiological model offers enhanced accuracy and predictive capabilities for blood volume dynamics.
- New methods for credibility assessment and physiological model selection were identified and merged.
- This research contributes to a more efficient and reliable evaluation process for PCLC medical devices.
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