Current Progress of COPD Early Detection: Key Points and Novel Strategies

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide, with approximately 70% to 80% of adults with COPD being undiagnosed. Patients with undiagnosed COPD are at increased risk of poor outcomes and a worsened quality of life, making early detection a crucial strategy to mitigate the impact of COPD and reduce the burden on healthcare systems. In the past decade, increased interest has been focused on the development of effective strategies and instrument for COPD early detection. However, identifying undiagnosed cases of COPD is still challenging. Both screening and case-finding approaches have been adopted to identify undiagnosed COPD, with case-finding being recommended by the 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) guideline and the updated United States Preventive Services Task Force (USPTF) recommendation. Nonetheless, the approaches, criteria, and instruments used for early detection of COPD are varied. However, advances in the taxonomy and risk factors of COPD are continuously being investigated. It is important to continuously assess the current state of knowledge on COPD early detection, given the challenges associated with identifying undiagnosed COPD. This review aims to highlight recent advances in early detection of COPD. To discuss the current challenge and opportunity in COPD early detection, providing an overview of existing literature on COPD case-finding strategies, including the approaches, criteria for subjects, and instruments. The review also summarizes the current progress in COPD case-findings and proposes a COPD case-finding flowchart as an efficient method for identifying at risk COPD patients.

Automated summary

Chronic obstructive pulmonary disease (COPD) is a significant global health issue, with a majority of cases going undiagnosed. Early detection is crucial to mitigate the impact of COPD, but identifying undiagnosed cases remains challenging.