Introduction COPD is a heterogeneous condition consisting of a number of different clinico-pathological subgroups (phenotypes), leading to particular challenges in managing the condition. Recognising these phenotypes may assist in directing the choice of treatment options. CT is being investigated as a tool for identifying key morphological features seen in COPD. Computer analysis of CT scans allows quantification of emphysema, bronchial wall thickening and gas trapping and offers the opportunity to study the heterogeneity of COPD.
This study aims to use quantitative digital software to analyse CT scans from a cohort of COPD patients to define clinically important phenotypes.
Methods Acute Exacerbation and Respiratory Infections in COPD (AERIS) is a longitudinal epidemiological study where patients with moderate to very severe COPD were followed monthly for 2 years. At enrolment subjects had pulmonary function testing and high resolution spiral CT was performed in inspiration and expiration. A sub-cohort of 36 patients is included in this analysis.
CT scans were reported by a thoracic radiologist using a validated scoring system for emphysema and gas trapping. Image analysis was performed using Apollo software. Emphysema was defined as the percent of lungs with low attenuation values below -950 Hounsfield Units (%LAA) on inspiratory scan. Airway wall thickness was standardised by using the square root of the wall area for a theoretical airway with an internal perimeter of 10 mm (AWT-Pi10). Gas trapping was calculated using the relative volume change of low attenuation areas from -856 to -950 between the inspiratory and expiratory scans (RVC856–950).
Results Correlation between the reported CT scores (emphysema and gas trapping) and corresponding quantitative measures (%LAA and RVC856–950) were strong: r = 0.79 and r = 0.5, respectively (p < 0.05). CT scores and quantitative measures for emphysema and gas trapping were significantly correlated with pulmonary function and BODE index (Table 1).
Conclusion In this study we have shown that quantitative chest CT measures correlate with a number of traditional physiological and prognostic markers in COPD. These measures have the potential to be clinically useful imaging biomarkers for the disease and further work will help validate this by investigating the longitudinal changes of the AERIS cohort.
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