An identifiable model of lung mechanics to diagnose and monitor COPD

Background: Current methods to diagnose and monitor COPD employ spirometry as the gold standard to identify lung function reduction with reduced forced expiratory volume (FEV1)/vital capacity (VC) ratio. Current methods utilise linear assumptions regarding airway resistance, where nonlinear resistance modelling may provide rapid insight into patient specific condition and disease progression. This study examines model-based expiratory resistance in healthy lungs and those with progressively more severe COPD.Methods: Healthy and COPD pressure (P)[cmH2O] and flow (Q)[L/s] data is obtained from the literature, and 5 intermediate levels of COPD and responses are created to simulate COPD progression and assess model-based metric resolution. Linear and nonlinear single compartment models are used to identify changes in inspiratory (R1,insp) and linear (R14xp)/nonlinear (R20) expiratory resistance with disease severity and over the course of expiration.Results: R1,insp increases from 2.1 to 7.3 cmH2O/L/s, R14xp increases from 2.4 to 10.0 cmH2O/L/s with COPD severity. Nonlinear R20 increases (mean R20: 2.5 cmH2O/L/s (healthy) to 24.4 cmH2O/L/s (COPD)), with increasing end-expiratory nonlinearity as COPD severity increases.Conclusion: Expiratory resistance is increasingly highly nonlinear with COPD severity. These results show a simple, nonlinear model can capture fundamental COPD dynamics and progression from regular breathing data, and such an approach may be useful for patient-specific diagnosis and monitoring.

Automated summary

This study examines the changes in expiratory resistance in healthy lungs and lungs of COPD patients. The results show that the severity of COPD is associated with increased nonlinearity in expiratory resistance. These findings suggest that a nonlinear model can accurately capture the progression of COPD from regular breathing data, offering potential clinical applications for patient-specific diagnosis and monitoring.