What question did this study set out to answer?

To evaluate the correlation between spirometry and forced oscillometry in COPD patients and assess the potential role of FOT in early diagnosis.

May 20, 2026

B78-12 Correlation Between Spirometry and Forced Oscillometry in Patients With Chronic Obstructive Pulmonary Disease

Key Points

To evaluate the correlation between spirometry and forced oscillometry in COPD patients and assess the potential role of FOT in early diagnosis.
Retrospective analysis of spirometry and FOT data of 99 COPD patients collected from November 2023 to August 2025.
Spirometry conducted using GANSHORN SpiroScout; FOT conducted using THORASYS tremoflo C-100.
Pearson’s correlation coefficients were calculated after applying Yeo-Johnson transformation due to non-normal data distribution.
FEV1 showed weak negative correlation with R5 (r = -0.274; p = 0.006) and R5-R20 (r = -0.392; p < 0.001).
Post-bronchodilator FEV1 changes had moderate negative correlation with changes in R5 (r = -0.495; p < 0.001).
Forced oscillometry showed modest correlation with spirometric indices, indicating its utility as a complementary diagnostic tool.

Abstract

Abstract Rationale Spirometry is effort dependent and needs patient cooperation. Forced oscillometry (FOT)is non-invasive, easier to perform, has advantage of able to differentiate large airway obstruction fromsmall airway obstruction. Therefore, it can be used in COPD patients who are unable to performspirometry and may help in earlier diagnosis of COPD. Materials and Methods This retrospective study analyzed spirometry and FOT data of COPDpatients recorded between November 2023 and August 2025. Spirometry was performed using theGANSHORN SpiroScout, and FOT using the THORASYS tremoflo C-100 Airwave OscillometrySystem. A sample size of 90 was estimated (90% CI, 80% power, 10% absolute error). As the datashowed non-normal distribution, Yeo-Johnson transformation was applied before calculatingPearson’s correlation coefficients. Results 99 patients (mean age 63 years, range 41-85; male:female ratio 3.9) were included. FEV1showed weak negative correlations with R5 (r = -0.274; p = 0.006) and R5-R20 (r = -0.392; p 0.001), moderate negative correlation with AX (r = -0.410; p 0.001), and weak positive correlationwith X5 (r = 0.307; p = 0.002). No significant correlation was noted between FEV1 and Fres (r =-0.265; p = 0.088). FEF25–75 correlated weakly with R5-R20 (r = -0.281; p = 0.005) and AX (r =-0.304; p = 0.002). Post-bronchodilator changes in FEV1 showed moderate negative correlationswith changes in R5 (r = -0.495; p 0.001) and R5-R20 (r = -0.468; p 0.001). Conclusion Forced oscillometry showed modest correlation with spirometric indices. The moderatecorrelation between post-bronchodilator changes in oscillometric and spirometric values supports itspotential utility in assessing bronchodilator responsiveness. Forced oscillometry may serve only as acomplementary tool but not a substitute to spirometry. This abstract is funded by: None

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