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Published on: February 9, 2022
Prediction of Abnormal Functional Performance in Chronic Obstructive Pulmonary Disease Using Respiratory Models: A
Caroline Oliveira Ribeiro1, Agnaldo José Lopes2, Pedro Lopes de Melo1,3
1Biomedical Instrumentation Laboratory - Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
Introduction:
The contribution of respiratory models to understanding and predicting functional capacity abnormalities in chronic obstructive pulmonary disease (COPD) has not yet been investigated.
Purpose:
The aims of this study were: (1) To investigate the associations between the extended Resistance-Inertance-Compliance (eRIC) and the fractional-order (FrOr) models with changes in Glittre-ADL and handgrip tests and; (2) To evaluate the accuracy of these models in predicting abnormal functional capacity in COPD.
Patients And Methods:
The study was carried out in a group of 40 adults with COPD and a control group of 40 healthy individuals, both evaluated by respiratory oscillometry, spirometry, Glittre-ADL test and handgrip test. eRIC and fractional order models were also used to quantify biomechanical changes and obtain physiological information. The ability of model parameters to predict abnormal functional performance was evaluated by investigating the area under the receiver operating characteristic curve (AUC).
Results:
Inverse relationships were observed between central airway resistance from the eRIC model and the handgrip test (p<0.005), while respiratory compliance (C) was directly related with handgrip strength test and inversely associated with the Glittre-ADL test time (p<0.05). The FrOr model showed direct associations among respiratory damping (G) and elastance with the Glittre-ADL test (p<0.02), while significant inverse relationships were observed with the handgrip test (p<0.05). Modeling parameters (peripheral resistance, total resistance and hysteresivity) achieved high prediction accuracy (AUC>0.90) in predicting non-normal functional capacity in COPD assessed by the Glittre-ADL test. Considering abnormal changes evaluated by the handgrip test as a reference, C (AUC=0.810) and G (AUC=0.786) obtained the highest predictive accuracies.
Conclusion:
Parameters obtained from the eRIC and the fractional order models are associated with non-normal exercise performance in COPD and may help predict poor functional performance in these patients.
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