Data based Modelling of Expired Airflow Clarifies Chronic Obstructive Pulmonary Disease

One of the major health challenges of the future is Chronic Obstructive Pulmonary Disease (COPD). It is characterized by airflow limitations, although current diagnosis does not give attention to the flow measurements. We aimed to develop a data-based model of the decline of the forced expiratory flow. Moreover, we analysed the relationship of model parameters with COPD presence and its severity. The data-based model was developed in 474 smoking individuals, who are at risk of having COPD, and have performed complete pulmonary function tests in order to identify whether the disease is present and at which stage. The time series of the decline of the flow was parameterised using the poles and steady state gain (SSG) of a second order transfer function model. These parameters were then linked with the presence of COPD. Observing SSG, median (IQR) in subjects with COPD was lower 3.9(2.7-5.6) compared to 8.2(7.1-9.3) in subjects without, (p<0.0001). Significant difference was also found when observing median (IQR) of two poles in subjects without disease were 0.9868(0.9810-0.9892) and 0.9333(0.9010-0.9529), respectively, compared to 0.9929(0.9901-0.9952) and 0.9082(0.8669-0.9398) in subjects with COPD (p<0.001 for both poles). Forced exhaled air can be used to expand understanding of the COPD. Moreover, the suggested parameterisation of the flow decline could be used to access COPD using spirometry.