Airspace Dimension Assessment with Nanoparticles (AiDA) in Comparison to Established Pulmonary Function Tests

Abstract

Background: Airspace Dimensions Assessment with nanoparticles (AiDA) is a new method for non-invasive measurement of pulmonary distal airspaces. The aim of this study was to compare AiDA measurements with other pulmonary function variables to better understand the potential of AiDA in a clinical context.

Methods: AiDA measurements and pulmonary function tests were performed in 695 subjects as part of the Swedish CArdioPulmonary bioImage Study. The measurement protocol included spirometry, measurement of diffusing capacity of carbon monoxide, oscillometry and pulmonary computed tomography. AiDA indices were compared to all other pulmonary examination measurements using multivariate statistical analysis.

Results: Our results show that AiDA measurements were significantly correlated with other pulmonary function examination indices, although covariance was low. We found that AiDA variables explained variance in the data that other lung function variables only influenced to a minor extent.

Conclusion: We conclude that the AiDA method provides information about the lung that is inaccessible with more conventional lung function techniques.

Keywords: COPD diagnostics; aerosol nanoparticles; airspace dimension assessment with nanoparticles; distal airspaces; respiratory diagnostics with nanoparticles.

Figure 1

Schematic diagram of the AiDA setup, example output data and nanoparticle recovery as a function of lung residence time. (A) Nanoparticles were electrospray-generated, size-selected and transferred to the reservoir from which the particle concentration was measured. The inhaled and exhaled nanoparticle concentrations were quantified with the same CPC. (B) Diagrams of a recorded breathing pattern and measured particle concentrations. The breathing-pattern and the inhaled and exhaled particle concentrations were analyzed to determine particle residence time in the lungs and the fraction of recovered particles after exhalation at each residence time. (C) A linear-least squares line was fitted to the recovery as a function of lung residence time, from which r_AiDA and R₀ were determined.

Figure 2

The r_AiDA and R₀ distributions for the 695 participants. (A) The r_AiDA distribution is skewed, largely due to the outliers at r_AiDA >400. Note that one participant had an r_AiDA value close to 600 µm. (B) The R₀ distribution approached a normal distribution with a mean recovery of 50% at an imaginary zero seconds breath-hold.

Figure 3

Explained variance per PC. The black line marks the cumulative explained variance while the bars show the explained variance per PC. Together the first five PCs explained 83% of the variance in the data, with each PC explaining 39%, 20%, 10%, 8% and 6% respectively, in descending order.

Figure 4

Heat map on the principal component factor loading for the first five PCs. The heatmap shows that the AiDA variables are not important components of the first two PCs, r_AiDA is important for PC3, and r_AiDA and R₀ are the dominating contributors to PC4 and PC5, with relatively low contributions from all other lung function variables. The variance in the data explained by PC4 and PC5 (14% of the variance in the data) is mainly explained by the AiDA variables.