Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality in the United States. Tobacco smoke is a critical environmental risk factor and in susceptible individuals it causes an exaggerated inflammatory response. The inflammatory response may ultimately destroy the lung parenchyma primarily generating emphysema and loss of elastic recoil and/or cause remodeling (or thickening) of the airway wall that increases airway resistance. Although these pathophysiological processes result in two contrasting phenotypes, their global effect on lung function may be extremely similar. The need to objectively identify and characterize the two COPD subclassifications has long been recognized because of the possibility to develop phenotypic treatment. Scientific discovery has made significant advances towards unraveling the cellular-molecular pathway associated with COPD, particularly regarding the immunopathogenesis. This understanding is critical to the development of potential COPD therapeutics, since therapy aimed at one phenotype could be ineffective or even contra-indicated for patients dominated by another (e.g., retinoic acid and interferon-gamma). Detection and characterization of the COPD phenotypes via a reasonable, non-invasive, in vivo methodology (e.g., CT examination) will be critical to identify treatment candidates and to quantify treatment efficacy. The purpose of this investigation is to develop, test and validate computer algorithms to assess the lung parenchyma and airways in over 1000 current and former smokers. Each subject will undergo biennial spirometry and CT examinations. We propose to develop a computerized scheme that objectively classifies patients into the COPD subclassifications based on lung function measured from spirometry as well as the relative contribution of the different components to the overall severity of disease. Using repeated spirometry ascertained over time we propose to develop a computerized scheme that provides predictive values on the likelihood of rapid disease progression. This proposal is aimed at improving detection and characterization of patients that suffer from chronic obstructive pulmonary disease using lung function measurements and CT examinations. This will be accomplished by developing computer algorithms that analyze the CT examinations to capture changes in lung function. If successful, the proposal will result in advanced methods to characterize pulmonary disease in the individual patient and to monitor the effectiveness of treatment as well.
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