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

Varghese Kurian, Michelle Gee, Sean Farrington, Entao Yang, Alphonse Okossi, Lucy Chen, Antony N Beris
Author Information
  1. Varghese Kurian: Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.
  2. Michelle Gee: Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.
  3. Sean Farrington: Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.
  4. Entao Yang: American Air Liquide Inc., Innovation Campus Delaware, Newark, Delaware 19702, United States. ORCID
  5. Alphonse Okossi: American Air Liquide Inc., Innovation Campus Delaware, Newark, Delaware 19702, United States.
  6. Lucy Chen: American Air Liquide Inc., Innovation Campus Delaware, Newark, Delaware 19702, United States.
  7. Antony N Beris: Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States. ORCID

Abstract

Recently, we developed a systems engineering model of the human cardiorespiratory system [Kurian et al. , (23), 20524-20535. DOI: 10.1021/acsomega.3c00854] based on existing models of physiological processes and adapted it for chronic obstructive pulmonary disease (COPD)-an inflammatory lung disease with multiple manifestations and one of the leading causes of death in the world. This control engineering-based model is extended here to allow for variable metabolic rates established at different levels of physical activity. This required several changes to the original model: the model of the controller was enhanced to include the feedforward loop that is responsible for cardiorespiratory control under varying metabolic rates (activity level, characterized as metabolic equivalent of the task--and normalized to one at rest). In addition, a few refinements were made to the cardiorespiratory mechanics, primarily to introduce physiological processes that were not modeled earlier but became important at high metabolic rates. The extended model is verified by analyzing the impact of exercise ( > 1) on the cardiorespiratory system of healthy individuals. We further formally justify our previously proposed adaptation of the model for COPD patients through sensitivity analysis and refine the parameter tuning through the use of a parallel tempering stochastic global optimization method. The extended model successfully replicates experimentally observed abnormalities in COPD-the drop in arterial oxygen tension and dynamic hyperinflation under high metabolic rates-without being explicitly trained on any related data. It also supports the prospects of remote patient monitoring in COPD.

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