Canadian respiratory journal · 2026
Quantitative Analysis of Bronchiectasis in Alpha-1 Antitrypsin Deficiency.
СтатьяНазвание и аннотация: Перевод в очереди
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De Soyza Joshua, Höger Philipp, Ellis Paul, Weinheimer Oliver, Trudzinski Franziska, Turner Alice M
Аннотация
INTRODUCTION: Alpha-1 antitrypsin deficiency (AATD) may be directly related to bronchiectasis. However, radiological diagnosis of mild bronchiectasis is subjective, and visual assessment of airway tapering is time-consuming. CT algorithms have been used to quantify airway tapering in other bronchiectasis aetiologies. We assessed whether this process can be used in AATD.
METHODS: Historic CT scans from patients with severe AATD in the Birmingham registry were processed with YACTA software (Weinheimer et al., 2017), and data on bronchiectasis index (BEI), airway wall thickness (AWPi10) and emphysema (PD15) were extracted. Correlation with visual data was assessed using standard statistical tests. Receiver operating characteristic (ROC) curves were used to find thresholds at which algorithm data are associated with visual data. Regression models assessed the impact of algorithm data on lung function decline, exacerbation rate, mortality and bronchiectasis severity index (BSI), corrected for age, sex, smoking status and PD15.
RESULTS: A total of 154 scans were analysed. BEI correlated moderately well with visual scoring of bronchial dilatation (p < 0.001), bronchial wall thickness (BWT, p < 0.001), number of lobes affected (p < 0.001) and morphology (p < 0.001). AWPi10 did not correlate with BWT (p = 0.28). Thresholds were identified for the association of BEI with bronchial dilatation ≥ 2 × arterial diameter; lobar involvement ≥ 3 lobes, and presence of varicose or cystic morphology, though sensitivity and specificity were relatively low. BEI is associated only with BSI (p = 0.002). AWPi10 did not associate with clinical outcomes.
CONCLUSION: Algorithm CT analysis has value in quantifying mild bronchiectasis in AATD and may be used in trials of emerging therapies.
METHODS: Historic CT scans from patients with severe AATD in the Birmingham registry were processed with YACTA software (Weinheimer et al., 2017), and data on bronchiectasis index (BEI), airway wall thickness (AWPi10) and emphysema (PD15) were extracted. Correlation with visual data was assessed using standard statistical tests. Receiver operating characteristic (ROC) curves were used to find thresholds at which algorithm data are associated with visual data. Regression models assessed the impact of algorithm data on lung function decline, exacerbation rate, mortality and bronchiectasis severity index (BSI), corrected for age, sex, smoking status and PD15.
RESULTS: A total of 154 scans were analysed. BEI correlated moderately well with visual scoring of bronchial dilatation (p < 0.001), bronchial wall thickness (BWT, p < 0.001), number of lobes affected (p < 0.001) and morphology (p < 0.001). AWPi10 did not correlate with BWT (p = 0.28). Thresholds were identified for the association of BEI with bronchial dilatation ≥ 2 × arterial diameter; lobar involvement ≥ 3 lobes, and presence of varicose or cystic morphology, though sensitivity and specificity were relatively low. BEI is associated only with BSI (p = 0.002). AWPi10 did not associate with clinical outcomes.
CONCLUSION: Algorithm CT analysis has value in quantifying mild bronchiectasis in AATD and may be used in trials of emerging therapies.