Correlation between impulse oscillometry parameters and asthma control in an adult population
- PMID: 31410030
- PMCID: PMC6643487
- DOI: 10.2147/JAA.S193744
Correlation between impulse oscillometry parameters and asthma control in an adult population
Abstract
Purpose: Impulse oscillometry (IOS) has been proposed as an alternative test to evaluate the obstruction of small airways and to detect changes in airways earlier than spirometry. In this study, we sought to determine the utility and association of IOS parameters with spirometry and asthma control in an adult population. Patients and methods: Adults 14-82 years of age with asthma were classified into uncontrolled asthma (n=48), partially controlled asthma (n=45), and controlled asthma (n=49) groups, and characterized with fractional exhaled nitric oxide (FENO), IOS, and spirometry in a transversal analysis planned as a one-visit study. The basic parameters evaluated in IOS are resistance at 5 Hz (R5), an index affected by the large and small airway; resistance at 20 Hz (R20), an index of the resistance of large airways; difference between R5 and R20 (R5-R20), indicative of the function of the small peripheral airways; reactance at 5 Hz (X5), indicative of the capacitive reactance in the small peripheral airways; resonance frequency (Fres), the intermediate frequency at which the reactance is null, and reactance area (XA), which represents the total reactance (area under the curve) at all frequencies between 5 Hz to Fres. Results: There were statistical differences between groups in standard spirometry and IOS parameters reflecting small peripheral airways (R5, R10, R5-R20, Fres, XA and X5) (P<0.001). Accuracy of IOS and/or spirometry to discriminate between controlled asthma vs partially controlled asthma and uncontrolled asthma was low (AUC=0.61). Using linear regression models, we found a good association between spirometry and IOS. In order to evaluate IOS as an alternative or supplementary method for spirometry, we designed a predictive model for spirometry from IOS applying a penalized regression model (Lasso). Then, we compared the original spirometry values with the values obtained from the predictive model using Bland-Altman plots, and the models showed an acceptable bias in the case of FEV1/FVC, FEV1%, and FVC%. Conclusion: IOS did not show a discriminative capacity to correctly classify patients according to the degree of asthma control. However, values of IOS showed good association with values of spirometry. IOS could be considered as an alternative and accurate complement to spirometry in adults. In a predictive model, spirometry values estimated from IOS tended to overestimate in low values of "real" spirometry and underestimate in high values.
Keywords: asthma; lung function tests; oscillometry; spirometry.
Conflict of interest statement
The authors report no conflicts of interest in this work.
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