Oscillometry in Asthma: Respiratory Modeling and Analysis in Occupational and Work-Exacerbated Phenotypes

Abstract

Monique da Silva Pinto,1 Caroline de Oliveira Ribeiro,1 Paula Morisco de Sá,2 Hermano Albuquerque Castro,3 Thiago Prudente Bártholo,4 Agnaldo José Lopes,4 Pedro Lopes Melo1,5 1Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil; 2University of the Brazilian Air Force, Postgraduate Program in Operational Human Performance, Rio de Janeiro, Brazil; 3National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil; 4Pedro Ernesto University Hospital, Pulmonary Function Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil; 5State University of Rio de Janeiro, Laboratory of Clinical and Experimental Research in Vascular Biology, Rio de Janeiro, BrazilCorrespondence: Pedro Lopes Melo, Department of Physiology, State University of Rio de Janeiro, São Francisco Xavier Street, Maracanã, Rio de Janeiro, Brazil, Tel [phone], Email [email protected]: Asthma onset or worsening of the disease in adulthood may be associated with occupational asthma (OA) or work-exacerbated asthma (WEA). Oscillometry and respiratory modeling offer insight into the pathophysiology and contribute to the early diagnosis of respiratory abnormalities.Purpose: This study aims to compare the changes due to OA and WEA and evaluate the diagnostic accuracy of this method.Patients and Methods: Ninety-nine volunteers were evaluated: 33 in the control group, 33 in the OA group, and 33 in the WEA group. The area under the receiver operator characteristic curve (AUC) was used to describe diagnostic accuracy.Results: Oscillometric analysis showed increased resistance at 4 hz (R4, p< 0.001), 20 hz (R20, p< 0.05), R4-R20 (p< 0.0001), and respiratory work (p< 0.001). Similar analysis showed reductions in dynamic compliance (p< 0.001) and ventilation homogeneity, as evaluated by resonance frequency (Fr, p< 0.0001) and reactance area (p< 0.0001). Respiratory modeling showed increased peripheral resistance (p< 0.0001), hysteresivity (p< 0.0001), and damping (p< 0.0001). No significant changes were observed comparing OA with WEA in any parameter. For OA, the diagnostic accuracy analyses showed Fr as the most accurate among oscillometric parameters (AUC=0.938), while the most accurate from respiratory modeling was hysteresivity (AUC=0.991). A similar analysis for WEA also showed that Fr was the most accurate among traditional parameters (AUC=0.972), and hysteresivity was the most accurate from modeling (AUC=0.987). The evaluation of differential diagnosis showed low accuracy.Conclusion: Oscillometry and modeling have advanced our understanding of respiratory abnormalities in OA and WEA. Furthermore, our study presents evidence suggesting that these models could aid in the early diagnosis of these diseases. Respiratory oscillometry examinations necessitate only tidal breathing and are straightforward to conduct. Collectively, these practical considerations, coupled with the findings of our study, indicate that respiratory oscillometry in conjunction with respiratory modeling, may enhance lung function assessments in OA and WEA.Keywords: occupational asthma, work-exacerbated asthma, pulmonary function, forced oscillations, respiratory modeling, fractional order modeling

Keywords

• occupational asthma
• work-exacerbated asthma
• pulmonary function
• forced oscillations
• respiratory modeling
• fractional order modeling