Abstract
Despite being a major cause of morbidity and mortality, chronic obstructive pulmonary disease (COPD) is frequently undiagnosed. Yet the burden of disease among the undiagnosed is significant, as these individuals experience symptoms, exacerbations, and excess mortality compared to those without COPD. The U.S. Preventive Services Task Force recommends against routine screening of asymptomatic individuals with spirometry. Hence, case-finding approaches are needed. A recently developed instrument, the five-item COPD Assessment in Primary Care to Identify Undiagnosed Respiratory Disease and Exacerbation Risk questionnaire plus peak expiratory flow, demonstrates good sensitivity and specificity for distinguishing cases from control subjects and is being studied prospectively in primary care settings to determine its impact on patient outcomes. However, finding the undiagnosed is only half the battle. Mounting evidence suggests significant COPD-like respiratory burden among individuals without airflow obstruction. Many experience dyspnea, mucus production, and exacerbation events and have emphysema and airway abnormalities on computed tomographic (CT) imaging of the chest. However, it is still unclear how to best treat these individuals and which individuals go on to develop spirometric obstruction. These challenges underline the importance of defining what constitutes "early disease." A recently proposed definition characterizes early COPD as either: 1) airflow limitation, 2) compatible CT imaging abnormalities, or 3) accelerated forced expiratory volume in 1 second decline in persons younger than 50 years and with greater than a 10 pack-year smoking history. Although it is recognized that this definition does not encompass all individuals who will develop COPD, it is an attempt to identify a group of individuals with most rapid decline to better understand mechanisms of disease development and where disease-modifying interventions are most likely to be successful. Ultimately, leveraging tools such as chest CT imaging, the electronic medical record, and machine learning algorithms may aid in the identification of such individuals.