ObjectiveTo propose a refined CT-based phenotypic classification of pulmonary bullae and investigate the differences in demographic characteristics, morphological features, and pulmonary function among different subtypes.MethodsThis retrospective study enrolled 467 patients diagnosed with pulmonary bullae who underwent chest CT at Tangdu Hospital from January 2023 to December 2024. Patients were classified into three phenotypes: bullous emphysema, intrapulmonary bullae, and subpleural bullae. Demographic data, smoking history, BMI, bulla morphology (size, number, internal septations, wall thickness, margin regularity), and pulmonary function parameters (FVC, FEV1, FEV1/FVC, DLCO) were collected and compared among groups. Subgroup analyses were performed based on smoking status, BMI level, and COPD status.ResultsSignificant intergroup differences were observed in age, BMI, and smoking history (P < 0.05). Patients with bullous emphysema were older, had lower BMI, and higher smoking rates. Morphologically, the bullous emphysema group exhibited significantly greater maximum bulla length, width, and surface area compared to the other two groups (P < 0.05). Smoking was associated with larger bullae, higher proportions of internal septations and thick walls, and fewer regular margins (P < 0.05). Low BMI patients (BMI < 23.03 kg/m2) demonstrated larger bullae with more complex morphology compared to high BMI patients (P < 0.05). Functionally, the bullous emphysema group had significantly lower FEV1, FEV1% predicted, FEV1/FVC, and DLCO% predicted (P < 0.05). Patients with COPD (n = 83) showed larger bullae and lower proportions of regular margins and internal septations compared to non-COPD patients (P < 0.05).ConclusionCT-based phenotyping of pulmonary bullae reveals distinct demographic, morphological, and functional profiles. Bullous emphysema is associated with more severe morphological abnormalities and pulmonary function impairment. Smoking and low BMI are modifiable factors promoting bulla progression. This classification provides a foundation for personalized clinical surveillance and intervention strategies.
Zou et al. (Wed,) studied this question.
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