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Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease Part II: Extension for

Varghese Kurian1, Michelle Gee1,2, Sean Farrington1

  • 1Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.

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A new systems engineering model simulates the human cardiorespiratory system, now including variable metabolic rates for exercise and chronic obstructive pulmonary disease (COPD) patients. This model accurately predicts COPD abnormalities during activity.

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Area of Science:

  • Systems Engineering
  • Physiological Modeling
  • Computational Biology

Background:

  • Chronic obstructive pulmonary disease (COPD) is a major global health issue with complex cardiorespiratory manifestations.
  • Existing models often lack the ability to accurately simulate cardiorespiratory responses during varying physical activity levels.
  • Accurate modeling of COPD is crucial for understanding disease progression and developing effective interventions.

Purpose of the Study:

  • To extend a systems engineering model of the human cardiorespiratory system to incorporate variable metabolic rates.
  • To adapt and validate the model for simulating cardiorespiratory function in individuals with COPD, particularly during physical activity.
  • To investigate the potential of the model for remote patient monitoring in COPD management.

Main Methods:

  • Enhancement of a control engineering-based cardiorespiratory model with a feedforward loop for metabolic rate control.
  • Refinement of cardiorespiratory mechanics to account for physiological processes relevant at high metabolic rates.
  • Verification through sensitivity analysis and parameter tuning using parallel tempering stochastic global optimization.

Main Results:

  • The extended model successfully simulates the impact of exercise on healthy individuals' cardiorespiratory systems.
  • The model replicates key COPD abnormalities, such as reduced arterial oxygen tension and dynamic hyperinflation, under high metabolic rates without explicit training data.
  • Sensitivity analysis formally justifies the model's adaptation for COPD patients.

Conclusions:

  • The enhanced model provides a robust platform for studying cardiorespiratory dynamics across different metabolic states.
  • The model's ability to predict COPD-specific exercise-induced abnormalities highlights its clinical relevance.
  • The developed model shows promise for applications in remote patient monitoring and personalized COPD management strategies.