Computed tomography-based measurements associated with rapid lung function decline in severe asthma.
Ann Allergy Asthma Immunol
; 2024 Sep 05.
Article
en En
| MEDLINE
| ID: mdl-39243811
ABSTRACT
BACKGROUND:
Severe asthma patients are susceptible to lung function decline (LFD), but biomarkers that reliably predict an accelerated LFD have not been fully recognized.OBJECTIVE:
In this study, we explored the computed tomography (CT) imaging features within pre-defined LFD groups to identify variables associated with previous LFD occurrences in severe asthma patients.METHODS:
We obtained inspiratory and expiratory CT image of 102 severe asthma patients and derived two airway structural parameters (wall thickness [WT] and hydraulic diameter [Dh]) and two parenchymal variables (functional small airway disease [fSAD] and emphysema [Emph]). We retrospectively calculated the annual changes in forced expiratory volume in 1 second and grouped participants by their values determined. The four-imaging metrics, along with levels of several biomarkers were compared among LFD groups.RESULTS:
Severe asthma patients with enhanced LFD exhibited significantly lower WT and smaller Dh compared to those with minimal change or slight decline in lung function, after an adjustment of smoking status. Conversely, CT-based percentages of Emph and fSAD 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 severe asthma patients with enhanced LFD compared to 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 severe asthma patients. Consequently, active management plans may help to attenuate LFD for severe asthma patients with lower WT and high FeNO.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Ann Allergy Asthma Immunol
Asunto de la revista:
ALERGIA E IMUNOLOGIA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos