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Structural Lung Disease and Clinical Phenotype in Bronchiectasis Patients: The EMBARC CT Study

Angelina L P Pieters^1,2, Tjeerd van der Veer³, Jennifer J Meerburg^1,2

¹Department of Radiology and Nuclear Medicine.

American Journal of Respiratory and Critical Care Medicine

|April 18, 2024

Summary

Structural lung abnormalities in bronchiectasis (BE) are diverse. Chest CT scans reveal varied disease extent, correlating with clinical factors like infections and lung function, aiding in patient phenotyping.

Area of Science:

Pulmonary Medicine
Radiology
Medical Imaging

Background:

Chest computed tomography (CT) is crucial for diagnosing and monitoring bronchiectasis (BE).
Quantitative data on structural lung abnormalities (SLAs) in BE patients via CT are limited.
Understanding these SLAs is vital for comprehensive patient assessment.

Purpose of the Study:

To investigate the nature and extent of SLAs on CT scans in BE patients.
To explore the relationship between SLAs and clinical features.
To utilize data from the European Multicenter Bronchiectasis Audit and Research Collaboration (EMBARC) registry.

Main Methods:

Analysis of CT scans from 524 BE patients in the EMBARC registry.
Application of the validated Bronchiectasis Scoring Technique for CT (BEST-CT) to quantify SLAs.
Calculation of composite scores for total BE, mucus plugging, inflammatory changes, and overall disease.

Main Results:

Significant heterogeneity in the type and extent of SLAs was observed.
BE associated with primary ciliary dyskinesia showed more SLAs; COPD was linked to fewer.
Lower FEV1, longer disease duration, specific infections, and severe exacerbations correlated with worse SLAs.

Conclusions:

SLAs in BE patients exhibit considerable variability.
Strong correlations between radiological findings and clinical characteristics were identified.
CT-based analysis of SLAs can serve as a valuable tool for clinical phenotyping in BE.

Keywords:

airway wall thickening artificial intelligence bronchial diseases bronchiectasis quantitative imaging analysis

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