Prediction of Spirometric Indices Using Forced Oscillometric Indices in Patients with Asthma, COPD, and Interstitial Lung Disease

BACKGROUND AND OBJECTIVE: Spirometry is sometimes difficult to perform in elderly patients and patients with cognitive impairment. Forced oscillometry (FOT) is a simple, noninvasive technique used for measuring respiratory impedance. The aim of this study was to develop regression equations to estimate vital capacity (VC), forced vital capacity (FVC), and forced expiratory volume in 1 s (FEV(1.0)) on the basis of FOT indices and to evaluate the accuracy of these equations in patients with asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung disease (ILD). MATERIALS AND METHODS: We retrospectively included data on 683 consecutive patients with asthma (388), COPD (128), or ILD (167) in this study. We generated regression equations for VC, FVC, and FEV(1.0) by multivariate linear regression analysis and used them to estimate the corresponding values. We determined whether the estimated data reflected spirometric indices. RESULTS: Actual and estimated VC, FVC, and FEV(1.0) values showed significant correlations (all r > 0.8 and P < 0.001) in all groups. Biases between the actual data and estimated data for VC, FVC, and FEV(1.0) in the asthma group were −0.073 L, −0.069 L, and 0.017 L, respectively. The corresponding values were −0.064 L, 0.027 L, and 0.069 L, respectively, in the COPD group and −0.040 L, −0.071 L, and −0.002 L, respectively, in the ILD group. The estimated data in the present study did not completely correspond to the actual data. In addition, sensitivity for an FEV(1.0)/FVC ratio of <0.7 and the diagnostic accuracy for the classification of COPD grade using estimated data were low. CONCLUSION: These results suggest that our method is not highly accurate. Further studies are needed to generate more accurate regression equations for estimating spirometric indices based on FOT measurements.