A model-based quantification of nonlinear expiratory resistance in Plethysmographic data of COPD patients.

Abstract

BackgroundChronic obstructive pulmonary disease (COPD) is characterised by airway obstruction with an increase in airway resistance (R) to airflow in the lungs. An extreme case of expiratory airway resistance is expiratory flow limitation, a common feature of severe COPD. Current analyses quantify expiratory R with linear model-based methods, which do not capture non-linearity's noted in COPD literature. This analysis utilises a simple nonlinear model to describe patient-specific nonlinear expiratory resistance dynamics typical of COPD and assesses its ability to both fit measured data and also to discriminate between severity levels of COPD. MethodsPlethysmographic data, including alveolar pressure and airway flow, was collected from n=100 subjects (40 healthy, 60 COPD) in a previous study. Healthy cohorts included Young (20-32 years) and Elderly (64-85 years) patients. COPD patients were divided into those with expiratory flow limitation (FL) and those without (NFL). Inspiratory R was treated as linear (R1,insp). Expiratory R was modelled with two separate models for a comparison: linear with constant resistance (R1,exp), and nonlinear time-varying resistance (R2,exp(t)) using b-splines. ResultsModel fit to PQ loops show inspiration is typically linear. Linear R1,exp captured expiratory dynamics in healthy cohorts (RMSE 0.3 [0.2 - 0.4] cmH2O), but did not capture nonlinearity in COPD patients. COPD cohorts showed PQ-loop ballooning during expiration, which was better captured by non-linear R2,exp(t) (RMSE 1.7[1.3-2.8] vs. 0.3[0.2-0.4] cmH2O in FL patients). Airway resistance is higher in COPD than healthy cohorts (mean R2,exp(t) for Young (1.9 [1.6-2.8]), Elderly (2.4 [1.4-3.5]), NFL (4.9 [3.9-6.6]) and FL (13.5 [10.4-21.9]) cmH2O/L/s, with p ≤ 0.0001 between aggregated measures for Young and Elderly healthy subjects and NFL and FL COPD subjects). FL patients showed non-linear R2,exp(t) dynamics during flow deceleration, differentiating them from NFL COPD patients. ConclusionsLinear model metrics describe expiration dynamics well in healthy subjects, but fail to capture nonlinear dynamics in COPD patients. Overall, the model-based method presented shows promise in detecting expiratory flow limitation, as well as describing different dynamics in healthy, COPD, and FL COPD patients. This method may thus be clinically useful in the diagnosis or monitoring of COPD patients using Plethysmography data, without the need for additional expiratory flow limitation confirmation procedures.