External validation of an IOS-derived model for airflow obstruction and COPD in a community-based cohort.

Abstract

A model based on impulse oscillometry (IOS) predicting airflow limitation and chronic obstructive pulmonary disease (COPD) has been developed in hospital-based populations. However, its diagnostic performance in community-recruited cohorts and its potential complementary value when combined with commonly used questionnaire-based tools remain to be evaluated. To externally validate the IOS-derived model for airflow obstruction (AO) and COPD in a community-based cohort, and to assess whether combining it with the COPD Screening Questionnaire (COPD-SQ) improves diagnostic accuracy compared to either tool alone. This was a cross-sectional analysis of baseline data from the ongoing Early Chronic Obstructive Pulmonary Disease (ECOPD) prospective cohort study. Participants completed pre-bronchodilator IOS tests, pre- and post-bronchodilator spirometry tests. The previously developed IOS-derived model (incorporating age, sex, height, weight, and IOS parameters) was applied to this Chinese population cohort. The COPD-SQ (7 items) was also administered. We defined a pre-bronchodilator FEV₁/FVC < 0.70 as airflow obstruction (AO) and a post-bronchodilator FEV₁/FVC < 0.70 as COPD. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the IOS-derived model, COPD-SQ, and their combination for AO and COPD. Differences in area under the curve (AUC) were compared using DeLong's test. A total of 1858 participants with complete IOS parameters were analyzed. The IOS-derived model showed acceptable diagnostic accuracy for AO (AUC 0.826, 95% CI 0.808-0.844, sensitivity 67.4%, specificity 80.6%) and COPD (AUC 0.836, 95% CI 0.818-0.854, sensitivity 71.6%, specificity 78.1%) in this community-based cohort. The combination of the IOS-derived model and COPD-SQ demonstrated modestly higher diagnostic accuracy for AO (AUC 0.839 vs 0.826 for IOS-derived model or 0.784 for COPD-SQ, all P < 0.001) and COPD (AUC 0.851 vs 0.836 or 0.795, all P < 0.001) compared to either tool alone. In this community-recruited cohort with a relatively high prevalence of airflow limitation and COPD, the IOS-derived model exhibited good diagnostic performance and, when combined with the COPD-SQ, showed modestly improved accuracy for identifying AO and COPD compared to single-modality approaches. These findings support further evaluation of the model in unselected general populations and real-world primary care settings.