Modelling the dynamics of expiratory airflow to describe chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation, but current diagnostic criteria only consider flow till the first second and are therefore strongly debated. We aimed to develop a data-based individualized model for flow decline and to explore the relationship between model parameters and COPD presence. A second-order transfer function model was chosen and the model parameters (namely the two poles and the steady state gain (SSG)) from 474 individuals were correlated with COPD presence. The capability of the model to predict disease presence was explored using 5 machine learning classifiers and tenfold cross-validation. Median (95% CI) poles in subjects without disease were 0.9868 (0.98580.9878) and 0.9333 (0.92560.9395), compared with 0.9929 (0.99250.9933) and 0.9082 (0.90040.9140) in subjects with COPD (p<0.001 for both poles). A significant difference was also found when analysing the SSG, being lower in COPD group 3.8 (3.54.2) compared with 8.2 (7.88.7) in subjects without (p<0.0001). A combination of all three parameters in a support vector machines corresponded with highest sensitivity of 85%, specificity of 98.1% and accuracy of 88.2% to COPD diagnosis. The forced expiration of COPD can be modelled by a second-order system which parameters identify most COPD cases. Our approach offers an additional tool in case FEV1/FVC ratio-based diagnosis is doubted.
chronic obstructive pulmonary disease machine learning forced expiration data-based modelling spirometry