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Pulmonary function and sputum characteristics predict computed tomography phenotype and severity of COPD
Gianna Camiciottoli1, Francesca Bigazzi, Matteo Paoletti
1Dept of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
The European Respiratory Journal
|December 22, 2012
Summary
Clinical and pulmonary function data can predict chronic obstructive pulmonary disease (COPD) severity and phenotype. Models using diffusing capacity and lung function accurately classify COPD patients by severity and pathological changes seen on CT scans.
Area of Science:
- Pulmonary Medicine
- Radiology
- Medical Imaging
Background:
- Chronic obstructive pulmonary disease (COPD) is characterized by airway obstruction and parenchymal destruction.
- The phenotype and severity of COPD are influenced by these pathological changes.
- Quantitative assessment of these changes using computed tomography (CT) is crucial for understanding COPD.
Purpose of the Study:
- To predict the predominant type and severity of pathological changes in COPD using clinical and pulmonary function data.
- To quantitatively assess airway wall thickness (AWT-Pi10) and lung area with low X-ray attenuation (%LAA-950) via CT.
- To develop models for classifying COPD patients based on CT-derived parameters.
Main Methods:
- Collected clinical and functional data from 473 COPD outpatients.
- Measured AWT-Pi10 and %LAA-950 in 100 patients (learning set).
- Used principal component analysis to derive CT1 and CT2 coordinates representing airflow limitation and severity.
- Developed cross-validated models using clinical and functional variables to predict CT1 and CT2.
- Classified 373 patients (testing set) using the developed models.
Main Results:
- A model based on diffusing capacity for carbon monoxide, total lung capacity, and purulent sputum predicted CT1 (r = 0.64, p<0.01).
- A model using forced expiratory volume in 1s/vital capacity, functional residual capacity, and purulent sputum predicted CT2 (r = 0.77, p<0.01).
- Predicted CT1 and CT2 values in the testing set correlated with clinical and functional variables, reflecting COPD phenotype and severity.
Conclusions:
- Multivariate models incorporating pulmonary function variables and sputum purulence can effectively classify COPD patients.
- These models predict overall COPD severity and the predominant pathological phenotype as assessed by quantitative CT.
- This approach offers a non-invasive method for characterizing COPD subtypes and severity.
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