Rationale: The Small Airways are a major site of obstruction in many respiratory diseases, including COPD. More insight into a diagnosis of Small Airways Dysfunction (SAD) in patients with COPD is clinically valuable as it might enable tailored pharmacotherapy. Currently, methods to diagnose SAD in COPD are not standardized and are not available in routine clinical practice. The Small Airways Dysfunction Tool (SADT) was developed to identify patients with asthma and SAD. Initially, the SADT included a comprehensive 63-item questionnaire. The number of items has been reduced to a SADT-asthma (SADT a) questionnaire and key patient and disease characteristics for it to be feasible and implementable in clinical practice. Although there are many similarities between asthma and COPD, there might be differences in clinical characteristics and responses to small airways dysfunction between the two diseases. The current study aims to adapt the original 63-item SADT questionnaire for dedicated use in COPD by reducing the number of items, and identifying COPD-SAD-specific items, to enhance its efficiency in identifying SAD when combined with key patient and disease characteristics in individuals with COPD (SADT-c). In addition, a comparison of diagnostic accuracy of spirometry and oscillometry will be made by interpretations by a panel of experts to provide a triage diagnosis. The previously developed machine learning AC/DC tool will be used to explore its diagnostic accuracy using oscillometry and spirometry results. This can contribute to standardizing oscillometry in clinical practice.
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The predictive value of SADT-c for detecting SAD in patients with COPD.
Timeframe: At baseline study visit, during pulmonary function testing