ABSTRACT A fundamental gap exists in our ability to screen for Chronic Obstructive Pulmonary Disease (COPD) that has resulted in widespread delayed diagnosis and underdiagnosis. COPD is now the third leading cause of death in the world, but how best to identify individuals remains a significant challenge, as symptoms alone are not sensitive enough to identify those at greatest risk. Hence, there is a critical need to find a better way to identify high risk individuals who require targeted pulmonary function testing. Providing a potential solution, lung cancer screening has now been widely adopted and is recommended by the US Preventive Task Force and paid for by the Center for Medicare and Medicaid Services, for individuals aged 50 to 80 years who have a 20 pack-year smoking history. Given the wealth of information that low-dose computed tomography (LDCT) for lung cancer screening provides, this represents an unprecedented opportunity to leverage what is now routinely collected data to improve the overall health of this patient population. Estimates suggest that roughly 35% of those undergoing lung cancer screening CT scans have undiagnosed COPD. Our group has already reported on a subset of individuals from the National Lung Cancer Screening Trial (NLST) that quantitative analysis of emphysema from LDCT can be used to help detect individuals with COPD. However, despite recognition of the problem, this solution has yet to be widely implemented. Radiologists remain unclear on what to tell primary care physicians; primary care physicians are also unclear on what to do with information they are provided, even when radiologists do mention the presence of emphysema in an LDCT report. Our overall objective with this proposal is to develop a tool based on data collected as part of lung cancer screening to enable clinicians to identify risk for COPD. Our central hypothesis is that a quantitative, imaging- based COPD risk tool can help identify undiagnosed patients with COPD. We will test our hypothesis through the following Specific Aims: In Aim 1 we will a) use quantitative imaging analysis to characterize the range of COPD-related pathology from LDCT scans from NLST and b) formulate an advanced multivariable model integrating diverse quantitative imaging attributes to determine the probability of COPD. In Aim 2, we will build a user-informed communication tool designed for clinicians but understandable by patients to facilitate screening for COPD by determining a) primary care provider beliefs, attitudes and approaches to the interpretation of LDCT findings; and b) patient understanding regarding risk for COPD and interpretation of lung cancer screening scans. Upon completion, the risk tool developed is expected to have an important positive impact by paving the way for future interventional studies with the tool that could be implemented at a health system level to reduce the burden of undiagnosed COPD.