A controlled statistical study to assess measurement variability as a function of test object position and configuration for automated surveillance in a multicenter longitudinal COPD study (SPIROMICS)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134827/1/mp7303.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134827/2/mp7303_am.pd

Multimodality imaging to quantify the pulmonary vascular tree in COPD

Chronic obstructive pulmonary disease (COPD) is a progressive and debilitating disease resulting in chronic cough, shortness of breath, activity limitation and decreased pulmonary function. Developments in imaging technology have provided sensitive and reliable modalities for evaluating regional lung function and disease progression, and there is a growing interest in the role of imaging the vasculature in COPD. The ability to predict whether a patient is at risk of accelerated decline is important to disease management strategies. We hypothesize that CT blood vessel volume measurements are significantly different in ex-smokers without COPD than in those with this disease and will be related to disease severity. 90 participants completed both baseline and follow-up visits: 41 ex-smokers without COPD (71±10yrs) and 49 participants with COPD (71±8yrs). From baseline to follow-up, RA950 increased significantly for ex-smokers and GOLD II participants, while PV1 decreased significantly for GOLD I. There were no differences in VDP when grouped according to change in FEV1. Participants whose FEV1 increased by more than 20mL/year experienced a significantly smaller change in RA950 compared to those whose FEV1 decreased by more than 40mL. Independent samples t-tests indicate a significant difference in the rate of PV1 progression between COPD groups with and without emphysema, but not VDP or RA950. Emphysema, or COPD phenotype, is related to vascular structure within the lung and the progression of vascular remodelling. Future work should include investigations of sex-differences in airways disease, and the use of machine learning to predict disease progression with optimized CT imaging parameters