Functional respiratory imaging in relation to classical outcome measures in cystic fibrosis: a cross-sectional study

Eline Lauwers,Kim Van Hoorenbeeck,Maarten Lanclus,Kris Ides,Stijn Verhulst,Annemiek Snoeckx,Wilfried De Backer,Jan De Backer

doi:10.1186/s12890-021-01622-3

Abstract

BackgroundFunctional Respiratory Imaging (FRI) combines HRCT scans with computational fluid dynamics to provide objective and quantitative information about lung structure and function. FRI has proven its value in pulmonary diseases such as COPD and asthma, but limited studies have focused on cystic fibrosis (CF). This study aims to investigate the relation of multiple FRI parameters to validated imaging parameters and classical respiratory outcomes in a CF population.MethodsCF patients aged > 5 years scheduled for a chest CT were recruited in a cross-sectional study. FRI outcomes included regional airway volume, airway wall volume, airway resistance, lobar volume, air trapping and pulmonary blood distribution. Besides FRI, CT scans were independently evaluated by 2 readers using the CF-CT score. Spirometry and the 6-Minute Walk Test (6MWT) were also performed. Statistical tests included linear mixed-effects models, repeated measures correlations, Pearson and Spearman correlations.Results39 CT scans of 24 (17M/7F) subjects were analyzed. Patients were 24 ± 9 years old and had a ppFEV1 of 71 ± 25% at the time of the first CT. All FRI parameters showed significant low-to-moderate correlations with the total CF-CT score, except for lobar volume. When considering the relation between FRI parameters and similar CF-CT subscores, significant correlations were found between parameters related to airway volume, air trapping and airway wall thickening. Air trapping, lobar volume after normal expiration and pulmonary blood distribution showed significant associations with all spirometric parameters and oxygen saturation at the end of 6MWT. In addition, air trapping was the only parameter related to the distance covered during 6MWT. A subgroup analysis showed considerably higher correlations in patients with mild lung disease (ppFEV1 ≥ 70%) compared to patients with moderate to severe lung disease (ppFEV1 < 70%) when comparing FRI to CF-CT scores.ConclusionsMultiple structural characteristics determined by FRI were associated with abnormalities determined by CF-CT score. Air trapping and pulmonary blood distribution appeared to be the most clinically relevant FRI parameters for CF patients due to their associations with classical outcome measures. The FRI methodology could particularly be of interest for patients with mild lung disease, although this should be confirmed in future research.

Highlights

Functional Respiratory Imaging (FRI) combines High resolution computed tomography (HRCT) scans with computational fluid dynamics to provide objective and quantitative information about lung structure and function
Additional file 1: Table S1 shows the patient characteristics for each individual study population separately, but no significant differences were noted between these three studies
18 chest Computed tomography (CT) from 12 subjects were categorized as mild lung disease ( Percentage predicted of FEV1 (ppFEV1) ≥ 70%), and 21 chest CTs from another 12 subjects as moderate to severe lung disease (ppFEV1 < 70%)

Summary

Introduction

Functional Respiratory Imaging (FRI) combines HRCT scans with computational fluid dynamics to provide objective and quantitative information about lung structure and function. Multiple scoring systems have shown to be reproducible and correlate with other respiratory outcomes [10, 11], they have several limitations. Most importantly, these methods rely on subjective observations to produce quantitative measures, which inevitably leads to considerable intra- and interobserver variability. To overcome many of the above-mentioned shortcomings, a smaller number of studies have described approaches for (semi-) automated quantification of low- or high attenuated areas and bronchial dilation [13,14,15,16] This type of approach is focused on a specific CT feature and the choice depends on the research question to be addressed [17]

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