(158f) Model-Based Diagnosis, Management, and Treatment of Chronic Obstructive Pulmonary Disease (COPD)

Model-Based
Diagnosis, Management, and Treatment of Chronic Obstructive Pulmonary Disease
(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 R&D, Newark, DE 19702

Abstract:

Chronic
Obstructive Pulmonary Disease (COPD) is a chronic lung disease characterized by
a persistent restriction of airflow through the lungs that is severe enough to
interfere with normal breathing function. Such airflow restrictions arise from
airway and/or alveolar abnormalities 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 2020 [2]. A 2010 report from the Centers for Disease Control and Prevention
(CDC) estimates the total medical costs attributed to COPD as $32B annually,
expected to rise to $49B by 2020 [3].

There
is as yet no cure for COPD and treatment is primarily focused on symptoms
management. A key challenge in
disease management is the occurrence of exacerbations—sustained worsening of the
patient’s condition beyond normal day-to-day variations—which may require a
change in medication and/or hospitalization, depending on the severity of the
episode. The ability to predict the occurrence and severity of these
exacerbations will facilitate effective management and treatment of the
disease. But the occurrence and severity of these exacerbations, and the consequent need for
hospitalization, have so far been impossible to
predict. Remote patient monitoring (RPM)—using digital technologies to collect
medical/health data from individuals in one location and transmit (securely) to
health care providers in a different location—has the potential to improve COPD
patient status assessment and treatment recommendations, and lower associated
healthcare costs. However, many clinical
trials and assessments—including those conducted by Air Liquide—have failed to
establish the clinical efficacy of RPM convincingly. To use RPM effectively for COPD patients we need to address
two key questions. First, which physiological variables are critical for, and
have the most significant impact on, effective COPD management? Second, how
often should these variables be sampled for effective COPD management? We seek
to answer these questions using an alternative model-based approach to complement
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 (sensor, controller, actuator, process) whose
physiological functions will be described by appropriate mathematical
equations.

In this presentation, we will discuss first the model development
and initial verification and then focus on analyzing the model for the explicit purpose of
identifying which model parameters are associated with the emergence of COPD.
Our model-based approach is unique
and significant in three distinct ways:  (i) it allows the objective
identification of physiological parameters associated with the occurrence of
COPD, and a quantification of the changes necessary for  the disease to
manifest; (ii) it facilitates the connection of fundamental physiological
responses of the cardiorespiratory system (blood partial pressure of oxygen,
blood partial pressure of carbon dioxide, and blood pH) to RPM sensor data
(heart rate, respiratory rate, blood pressure, SpO₂ and CO₂
measurements, etc.), ultimately allowing us to combine the model predictions
with RPM sensor data in order to predict the occurrence and potential severity
of exacerbations; (iii) the model-based framework is ideal for customizing
treatment because model parameters can be estimated uniquely for each patient
from the patient data.

Keywords: chronic obstructive pulmonary
disease, exacerbation, systems biology, 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.