Impulse oscillometric features and parsimonious respiratory models track small airway function in Hispanic and Anglo children

Abstract

Asthma is an inflammatory condition of the peripheral (small) airways resulting in airway hyper-reactivity and, among other symptoms, airflow obstruction. It is the most prevalent chronic respiratory disease in children. Reliable and patient-friendly instruments and methods are required to help pulmonologists accurately detect asthma and Small Airway Impairment (SAI) with acceptable clinical accuracy, specificity and sensitivity. Impulse Oscillometry System (IOS) based on the Forced Oscillation Technique (FOT) has been successfully used to measure lung function in children with a high degree of sensitivity and specificity to SAI and Asthma. IOS is a patient-friendly lung function to measure the mechanical impedance of the respiratory system. Equivalent electrical circuit models of lung function have been developed that can be used to quantify severity of SAI. It has been shown that impulse oscillometric parameters as well as parameter estimates of these equivalent electrical circuit models provide useful indicators of lung function and therefore have the potential to be used as sensitive features for computer-aided classification of pulmonary function in health and disease. Previous work by our group has evaluated several known respiratory models and two parsimonious versions known as extended RIC (eRIC) and augmented RIC (aRIC) models have emerged which offer advantages over earlier models.

This doctoral research aims to analyze IOS data acquired from Anglo and Hispanic children during pre- and post-bronchodilation conditions, as well as use the eRIC and aRIC model estimated parameters to determine which ones are better to differentiate between constricted and non-constricted lung conditions. It is also the first attempt to establish reference values for North American Anglo and Hispanic children 5 to 19 years old, and find correlations between IOS and eRIC and aRIC model parameters. The overall objective is to develop a user-friendly tool to assist clinicians in the analysis and interpretation of IOS data to better detect, diagnose, and treat asthma conditions. In the near future, this approach offers the potential to be used for computer-aided classification of pulmonary diseases.