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Development of a tool to detect small airways dysfunction in asthma clinical practice
PDPI Lampung & Bengkulu, 20 Jan 2023 10:57:19

Janwillem Kocks, Thys van der Molen, Jaco Voorham, Simonetta Baldi, Maarten van den Berge, Chris Brightling, Leonardo M. Fabbri, Monica Kraft, Gabriele Nicolini, Alberto Papi, Klaus F. Rabe, Salman Siddiqui, Dave Singh, Judith Vonk, Marika Leving, Bertine Flokstra-de Blok
European Respiratory Journal 2022; DOI: 10.1183/13993003.00558-2022


Background Small airways dysfunction (SAD) in asthma is difficult to measure and a gold standard is lacking. The aim of this study was to develop a simple tool including items of the small airways dysfunction tool (SADT) questionnaire, basic patient characteristics and respiratory tests available depending on clinical setting, to predict SAD in asthma.

Methods This study was based on the data of the multinational ATLANTIS (Assessment of Small Airways Involvement in Asthma) study including the earlier developed SADT questionnaire. Key SADT-items together with clinical information was now used to build logistic regression models to predict SAD group (less likely or more likely to have SAD). Diagnostic ability of the models was expressed as area under the receiver operating characteristic curve (AUC) and positive likelihood ratios (LR+).

Results SADT-item 8, “I sometimes wheeze when I am sitting or lying quietly”, and the patient characteristics age, age at asthma diagnosis and BMI could reasonably well detect SAD (AUC:0.74, LR+:2.3). The diagnostic ability increased by adding spirometry (FEV1pp; AUC:0.87, LR+:5.0) and oscillometry (R5-R20 and AX; AUC:0.96, LR+:12.8).

Conclusion If access to respiratory tests is limited (e.g. primary care in many countries), patients with SAD could reasonably well be identified by asking about wheezing at rest and a few patient characteristics. In (advanced) hospital settings patients with SAD could be identified with considerably higher accuracy using spirometry and oscillometry.

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