Computed tomography-based measurements associated with rapid lung function decline in severe asthma

Background: Patients with severe asthma are susceptible to lung function decline (LFD), but biomarkers that reliably predict an accelerated LFD have not been fully recognized. Objective: To identify variables associated with previous LFD occurrences in patients with severe asthma by exploring the computed tomography (CT) imaging features within predefined LFD groups. Methods: We obtained inspiratory and expiratory CT images of 102 patients with severe asthma and derived 2 airway structural parameters (wall thickness [WT] and hydraulic diameter) and 2 parenchymal variables (functional small airway disease and emphysema). We retrospectively calculated the annual changes in forced expiratory volume in 1 second and grouped participants by their values determined. The 4-imaging metrics, along with levels of several biomarkers, were compared among the LFD groups. Results: Patients with severe asthma with enhanced LFD exhibited significantly lower WT and smaller hydraulic diameter compared with those with minimal change or slight decline in lung function, after an adjustment of smoking status. Conversely, CT-based percentages of emphysema and functional small airway disease did not significantly differ according to LFD. Furthermore, fractional exhaled nitric oxide (FeNO) level and the blood matrix metalloproteinase-9/TIMP metallopeptidase inhibitor 1 ratio were significantly higher in patients with severe asthma with enhanced LFD compared with those in the others. Conclusion: Lower WT on CT scans with increased FeNO that may represent increased airway inflammation significantly correlated with enhanced LFD in patients with severe asthma. Consequently, active management plans may help to attenuate LFD for patients with severe asthma with lower WT and high FeNO. (c) 2024 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.