Computer quantification of airway collapse on forced expiration to predict the presence of emphysema

Abstract

Background: Spirometric parameters are the mainstay for diagnosis of COPD, but cannot distinguish airway obstruction from emphysema. We aimed to develop a computer model that quantifies airway collapse on forced expiratory flow-volume loops. We then explored and validated the relationship of airway collapse with computed tomography (CT) diagnosed emphysema in two large independent cohorts.

Methods: A computer model was developed in 513 Caucasian individuals with ≥15 pack-years who performed spirometry, diffusion capacity and CT scans to quantify emphysema presence. The model computed the two best fitting regression lines on the expiratory phase of the flow-volume loop and calculated the angle between them. The collapse was expressed as an Angle of collapse (AC) which was then correlated with the presence of emphysema. Findings were validated in an independent group of 340 individuals.

Results: AC in emphysema subjects (N = 251) was significantly lower (131° ± 14°) compared to AC in subjects without emphysema (N = 223), (152° ± 10°) (p < 0.0001). Multivariate regression analysis revealed AC as best indicator of visually scored emphysema (R2 = 0.505, p < 0.0001) with little significant contribution of KCO, %predicted and FEV1, %predicted to the total model (total R2 = 0.626, p < 0.0001). Similar associations were obtained when using CT-automated density scores for emphysema assessment. Receiver operating characteristic (ROC) curves pointed to 131° as the best cut-off for emphysema (95.5% positive predictive value, 97% specificity and 51% sensitivity). Validation in a second group confirmed the significant difference in mean AC between emphysema and non-emphysema subjects. When applying the 131° cut-off, a positive predictive value of 95.6%, a specificity of 96% and a sensitivity of 59% were demonstrated.

Conclusions: Airway collapse on forced expiration quantified by a computer model correlates with emphysema. An AC below 131° can be considered as a specific cut-off for predicting the presence of emphysema in heavy smokers.

Figure 1

Example of the calculation process with fitting of regression lines to the original data in incremental steps of 10 data points. Panels A-B show regression lines for a candidate reference point after respectively 10 and 400 data points. Panel C shows all regression lines together with a highlighted selection of 2 lines in yellow (after 6 steps) having the lowest MSE of all steps, as represented in Panel D.

Figure 2

Examples of successfully calculated angles of collapse in different subjects: (A/ AC = 162° B/ AC = 133°C/ AC = 107°).

Figure 3

Receiver operating characteristic (ROC) curve analysis. Panel A/ ROC curve of AC to diagnose emphysema. Panels B-E/ Scatter graph of different variables within emphysema subjects and non-emphysema subjects. The horizontal line represents the best cut-off for sensitivity-specificity.