Severity-based evaluation of dynamic airway collapse and tracheobronchomalacia: integrated clinical, functional and...

Excessive dynamic airway collapse (EDAC) and tracheobronchomalacia are increasingly recognised causes of expiratory airflow limitation, although their clinical relevance remains a matter of ongoing debate, particularly regarding the threshold defining significant collapse. While a ≥50% reduction in airway lumen has been traditionally used, this cut-off lacks specificity. This study aimed to evaluate the clinical, functional and endoscopic characteristics of dynamic airway collapse according to severity and to develop an integrated diagnostic-therapeutic approach.

A prospective observational study was conducted in 74 patients undergoing flexible bronchoscopy between January 2023 and March 2025. Clinical, anthropometric, functional, endoscopic and polygraphic variables were collected. Airway collapse was assessed during dynamic bronchoscopy and analysed in relation to clinical features and therapeutic interventions, including CPAP, using χ², ANOVA and Pearson correlation.

Dynamic airway collapse ≥50% was identified in 67.6% of patients, predominantly with combined tracheobronchial involvement. Additional analyses using contemporary severity thresholds demonstrated that most affected patients fulfilled the ≥70% criterion for clinically significant disease. Higher degrees of airway collapse were significantly associated with higher body mass index (r = 0.28; p = 0.015) and CPAP initiation (r = 0.326; p = 0.0046). Among patients receiving CPAP, 88.2% reported clinical improvement. Notably, 58.5% of patients with significant airway collapse had normal spirometry. Obstructive sleep apnea was highly prevalent (84%), with no significant correlation between apnea-hypopnea index and airway collapse severity.

Dynamic airway collapse is a frequent and clinically relevant finding in patients with respiratory symptoms. Our findings challenge the conventional ≥50% threshold, demonstrating that disease severity rather than a fixed cut-off better defines clinical relevance and directly informs therapeutic decision-making. The proposed severity-based algorithm provides a structured and clinically applicable framework for diagnosis and management, although external validation is required.