: Rationale: Chest computed tomography (CT) scans are essential to diagnose and monitor bronchiectasis (BE). To date, few quantitative data are available about the nature and extent of structural lung abnormalities (SLAs) on CT scans of patients with BE. Objectives: To investigate SLAs on CT scans of patients with BE and the relationship of SLAs to clinical features using the EMBARC (European Multicenter Bronchiectasis Audit and Research Collaboration) registry. Methods: CT scans from patients with BE included in the EMBARC registry were analyzed using the validated Bronchiectasis Scoring Technique for CT (BEST-CT). The subscores of this instrument are expressed as percentages of total lung volume. The items scored are atelectasis/consolidation, BE with and without mucus plugging (MP), airway wall thickening, MP, ground-glass opacities, bullae, airways, and parenchyma. Four composite scores were calculated: total BE (i.e., BE with and without MP), total MP (i.e., BE with MP plus MP alone), total inflammatory changes (i.e., atelectasis/consolidation plus total MP plus ground-glass opacities), and total disease (i.e., all items but airways and parenchyma). Measurements and Main Results: CT scans of 524 patients with BE were analyzed. Mean subscores were 4.6 (range, 2.3-7.7) for total BE, 4.2 (1.2-8.1) for total MP, 8.3 (3.5-16.7) for total inflammatory changes, and 14.9 (9.1-25.9) for total disease. BE associated with primary ciliary dyskinesia was associated with more SLAs, whereas chronic obstructive pulmonary disease was associated with fewer SLAs. Lower FEV1, longer disease duration, Pseudomonas aeruginosa and nontuberculous mycobacterial infections, and severe exacerbations were all independently associated with worse SLAs. Conclusions: The type and extent of SLAs in patients with BE are highly heterogeneous. Strong relationships between radiological disease and clinical features suggest that CT analysis may be a useful tool for clinical phenotyping.

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

Aliberti, Stefano;
2024-01-01

Abstract

: Rationale: Chest computed tomography (CT) scans are essential to diagnose and monitor bronchiectasis (BE). To date, few quantitative data are available about the nature and extent of structural lung abnormalities (SLAs) on CT scans of patients with BE. Objectives: To investigate SLAs on CT scans of patients with BE and the relationship of SLAs to clinical features using the EMBARC (European Multicenter Bronchiectasis Audit and Research Collaboration) registry. Methods: CT scans from patients with BE included in the EMBARC registry were analyzed using the validated Bronchiectasis Scoring Technique for CT (BEST-CT). The subscores of this instrument are expressed as percentages of total lung volume. The items scored are atelectasis/consolidation, BE with and without mucus plugging (MP), airway wall thickening, MP, ground-glass opacities, bullae, airways, and parenchyma. Four composite scores were calculated: total BE (i.e., BE with and without MP), total MP (i.e., BE with MP plus MP alone), total inflammatory changes (i.e., atelectasis/consolidation plus total MP plus ground-glass opacities), and total disease (i.e., all items but airways and parenchyma). Measurements and Main Results: CT scans of 524 patients with BE were analyzed. Mean subscores were 4.6 (range, 2.3-7.7) for total BE, 4.2 (1.2-8.1) for total MP, 8.3 (3.5-16.7) for total inflammatory changes, and 14.9 (9.1-25.9) for total disease. BE associated with primary ciliary dyskinesia was associated with more SLAs, whereas chronic obstructive pulmonary disease was associated with fewer SLAs. Lower FEV1, longer disease duration, Pseudomonas aeruginosa and nontuberculous mycobacterial infections, and severe exacerbations were all independently associated with worse SLAs. Conclusions: The type and extent of SLAs in patients with BE are highly heterogeneous. Strong relationships between radiological disease and clinical features suggest that CT analysis may be a useful tool for clinical phenotyping.
2024
airway wall thickening
artificial intelligence
bronchial diseases
bronchiectasis
quantitative imaging analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/91403
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