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Forced oscillation, integer and fractional-order modeling in asthma.

Alvaro C D Faria1, Juliana Veiga1, Agnaldo J Lopes2

  • 1Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), State University of Rio de Janeiro, Rio de Janeiro, Brazil.

Computer Methods and Programs in Biomedicine
|April 5, 2016
PubMed
Summary

Fractional-order (FrOr) modeling offers a superior, non-invasive method for detecting airway obstruction in asthma. This approach shows high accuracy in identifying mild, moderate, and severe obstructions, outperforming traditional lung function tests.

Keywords:
AsthmaConstant phase elementForced oscillation techniqueFractional-order modelRespiratory impedanceRespiratory system

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Area of Science:

  • Respiratory Medicine
  • Biomedical Engineering
  • Pulmonary Physiology

Background:

  • Asthma diagnosis relies on lung function tests, but accurately assessing airway obstruction, especially in mild cases, remains challenging.
  • Traditional models may not fully capture the complex biomechanics of airway obstruction in asthma.

Purpose of the Study:

  • To evaluate fractional-order (FrOr) modeling for assessing airway obstruction in asthma.
  • To compare FrOr models with traditional integer-order (InOr) models and spirometry.
  • To determine the diagnostic potential of FrOr parameters for airway obstruction.

Main Methods:

  • Forced oscillation (FO) measurements were collected from healthy individuals and asthmatic patients with varying obstruction severity.
  • Three FrOr models were compared against an InOr model for data fitting.
  • Correlation analyses with spirometry and receiver-operator characteristic (ROC) analyses were performed.

Main Results:

  • FrOr models provided a better fit to the FO data compared to the InOr model.
  • FrOr parameters demonstrated reasonable to very good correlations with spirometry, particularly for peripheral airway obstruction.
  • FrOr parameters achieved high accuracy (AUC=0.954) in detecting mild asthma, outperforming traditional FO parameters (AUC=0.732) and the InOr model (AUC=0.861).
  • High accuracy was also observed for moderate (AUC=0.972) and severe (AUC=0.977) obstructions.

Conclusions:

  • Fractional-order modeling is a highly accurate, non-invasive, and radiation-free method for detecting biomechanical abnormalities in asthma.
  • FrOr modeling shows significant potential for improving the clinical diagnosis of airway obstruction in asthma.
  • FrOr models offer a more sensitive approach to characterizing lung mechanics in asthma compared to traditional methods.