#00035Quantitative computed tomography measures of disease severity in cystic fibrosis

Introduction

Patients with cystic fibrosis (CF) are at high risk for respiratory infections. Forced expiratory volume in the first second (FEV1) assesses disease severity and progression in CF but is associated with measurement variability. Computational image analysis can objectively and accurately measure lung structural changes on computed tomography (CT) scans. 

Aim

To identify patients at high-risk for infection using automated CT measures of lung damage.

Methods

Patent and mucus plugged airways were automatically segmented from CT scans using a computer model trained on 12 manually labelled CTs (Figure 1). Volumes of patent and plugged airways were compared to contemporaneous FEV1 and C-reactive protein level (CRP) levels, and days of intravenous antibiotic use in the following 12 months.

Results
In 241 patients, plugged airway volume associated with baseline FEV1 (r=-0.28, p<0.0001). Plugged airway volume (r=0.36, p<0.0001) associated more strongly than FEV1 (r=-0.14, p=0.041) with days of antibiotic use. Patients with a CRP>75mg/L had a higher volume of patent and plugged airways (p=0.019 and p<0.0001, respectively) indicating severe airway damage, with no difference in FEV1 (p=0.11).

Conclusion

Image-derived biomarkers such as volume of mucus plugged airways can identify CF patients at high-risk for future infections who may benefit from closer monitoring, earlier antibiotic therapy and lung clearance measures.

[figure1]