Mortality by Level of Emphysema and Airway Wall Thickness
- 15 March 2013
- journal article
- Published by American Thoracic Society in American Journal of Respiratory and Critical Care Medicine
- Vol. 187 (6), 602-608
- https://doi.org/10.1164/rccm.201209-1722oc
Abstract
There is limited knowledge of the prognostic value of quantitative computed tomography (CT) measures of emphysema and airway wall thickness (AWT) on mortality. To examine 8-year mortality in relation to CT-measured emphysema and AWT, and assess if potential impact of these predictors remained after adjustment for lung function. In the Norwegian GenKOLS study of 2003-2005, 947 ever-smokers (49% with COPD) aged 40-85 years performed spirometry and CT examination. Mortality data from 2003-2011 were gathered from the Norwegian Cause of Death Registry. CT emphysema % low-attenuation areas (%LAA) and standardized measure for AWT (AWT-Pi10) were main predictors. We performed Laplace regression for survival data, estimating survival time for specified population percentiles within each emphysema category. Models were adjusted for sex, FEV1, COPD status, age, body mass index, smoking, and inflation level. During 8-year follow-up all-cause mortality rate was 15%. Although 4% of the subjects with %LAA less than 3 died, 18% with %LAA 3-10 and 44% with %LAA greater than or equal to 10 died. After adjustment, the comparable percentile subjects with medium and high emphysema had 19 months shorter survival than subjects who died in the lowest emphysema category. Subjects with %LAA greater than or equal to 10 had 33 and 37 months shorter survival than the lowest emphysema category with regard to respiratory and cardiovascular mortality, respectively. No significant associations were found between %LAA and cancer and lung cancer mortality. AWT did not predict mortality independently, but a positive interaction with emphysema was observed. AWT affected mortality with increasing degree of emphysema, whereas CT measure of emphysema was a strong independent mortality predictor.Keywords
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