• Gianna Camiciottoli, Francesca Bigazzi, Matteo Paoletti, Lucia Cestelli, Federico Lavorini, and Massimo Pistolesi.
• Dept of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
• Eur. Respir. J. 2013 Sep 1;42(3):626-35.

AbstractAirway obstruction and parenchymal destruction underlie phenotype and severity in chronic obstructive pulmonary disease (COPD). We aimed to predict, by clinical and pulmonary function data, the predominant type and severity of pathological changes quantitatively assessed by computed tomography (CT). Airway wall thickness (AWT-Pi10) and percentage of lung area with X-ray attenuation values <-950 HU (%LAA-950) were measured in 100 (learning set) out of 473 COPD outpatients undergoing clinical and functional evaluation. Original CT measurements were translated by principal component analysis onto a plane with the novel coordinates CT1 and CT2, depending on the difference (prevalent mechanism of airflow limitation) and on the sum (severity) of AWT-Pi10 and %LAA-950, respectively. CT1 and CT2, estimated in the learning set by cross-validated models of clinical and functional variables, were used to classify 373 patients in the testing set. A model based on diffusing capacity of the lung for carbon monoxide, total lung capacity and purulent sputum predicted CT1 (r = 0.64; p<0.01). A model based on forced expiratory volume in 1 s/vital capacity, functional residual capacity and purulent sputum predicted CT2 (r = 0.77; p<0.01). Classification of patients in the testing set obtained by model-predicted CT1 and CT2 reflected, according to correlations with clinical and functional variables, both COPD phenotype and severity. Multivariate models based on pulmonary function variables and sputum purulence classify patients according to overall severity and predominant phenotype of COPD as assessed quantitatively by CT.

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