(190bm) A Systems Engineering Framework for Diagnosis and Treatment of Chronic Obstructive Pulmonary Diseases (COPD)

A
Systems Engineering Framework for Diagnosis and Treatment of Chronic
Obstructive Pulmonary Diseases (COPD)

Navid Ghadipasha^a, Anais Chalant^b, Bin Yu^b,
Babatunde A. Ogunnaike^a[1]

^a Department of Chemical & Biomolecular Engineering,
University of Delaware, Newark, DE 19716

^b Air Liquide’s Delaware Research and Technology Center, Newark,
DE 19702

Abstract:

Chronic
Obstructive Pulmonary Disease (COPD), a chronic inflammatory lung disease that
causes obstructed airflow from the lungs, is caused by long-term exposure to
noxious particles or irritating gases such as cigarette smoke [1]. Currently the 4^th
leading cause of death worldwide, COPD is projected to become the third leading
cause by 2030 [2]. There is currently no cure for the disease and treatment is
primarily focused on symptoms management. Due to its progressive nature, many COPD patients are unaware of the
extent of their disability and are unable to recognize worsening symptoms.

According
to the Center for Disease Control (CDC), COPD accounted for $32B in medical
costs in 2010, with a projected increase to $49B by 2020, 70% of which is due
to hospitalization costs [3]. COPD patients often experience “exacerbations”,
which accelerate the decline in lung function. Depending on the exacerbation
severity, the patient may require hospitalization or a change in medication.
Remote patient monitoring (RPM)—digital technology for collecting relevant
health data from individual patients in one location (usually their homes) and
electronic transmission of the information to health care providers for
assessment and recommendations—can, in principle, increase access to care and
reduce hospitalization costs for COPD patients. However, after many clinical
trials and studies—including those conducted by Air Liquide—the clinical
efficacy of RPM has yet to be established convincingly. We propose an
alternative model-based approach to RPM, where an appropriate mechanistic model
of COPD is used in conjunction with data from medical devices to predict
patient responses. Specifically, we propose modeling the occurrence of COPD
from a control engineering perspective, whereby the cardio-respiratory system
is represented as a control system with components whose physiological
functions will be described by appropriate mathematical equations.

This approach allows us
to view COPD appropriately as arising from a defect in this physiological
control system. Consequently, a systematic analysis of the validated
computational model, using systems engineering tools and available clinical
data, will allow us to do the following: (i) identify the control system
component, physiological variables, and parameters that are associated with
reproducing clinical manifestations of COPD and associated exacerbations, and
hence provide insight into which vital signs should be monitored in COPD
patients; (ii) predict with greater accuracy and precision the occurrence,
timeline and severity of exacerbations; and (iii) propose testable hypotheses
about COPD treatment strategies personalized to each patient using insight
about the identified “malfunctioning component”, and model predictions. We believe that the ability to predict a potential
exacerbation, as well as its timeline and severity, will facilitate timely
access to healthcare facilities, better management of exacerbations, and
enhanced quality of life for COPD patients, thereby enhancing the clinical
efficacy of RPM systems.

Keywords: pulmonary disease, systems biology,
feedback control system, remote patient monitoring

References

[1] Mannino,
D. M., & Buist, A. S. (2007). Global burden of COPD: risk factors,
prevalence, and future trends. The Lancet, 370(9589),
765-773.

[2]
World Health Organization. (2011).
COPD predicted to be third leading cause of death in 2030. 2013-02-12].
http://www. who. int/respiratory/copd/World _ Health _ Statistics _
2008/en/index, html.

[3] Mulpuru, S., McKay, J., Ronksley, P. E.,
Thavorn, K., Kobewka, D. M., & Forster, A. J. (2017). Factors contributing
to high-cost hospital care for patients with COPD. International
journal of chronic obstructive pulmonary disease, 12, 989.