Identification of subtypes in subjects with mild-to-moderate airflow limitation and its clinical and socioeconomic implications

PURPOSE The purpose of this study was to identify subtypes in patients with mild-to-moderate airflow limitation and to appreciate their clinical and socioeconomic implications. METHODS Subjects who were aged ≥20 years and had forced expiratory volume in 1 second (FEV1) ≥60% predicted and FEV1/forced vital capacity <0.7 were selected from the fourth Korea National Health and Nutrition Examination Survey (KNHANES) in 2007-2012. The data were merged to the National Health Insurance reimbursement database during the same period. k-Means clustering was performed to explore subtypes. For clustering analysis, six key input variables - age, body mass index (BMI), FEV1% predicted, the presence or absence of self-reported wheezing, smoking status, and pack-years of smoking - were selected. RESULTS Among a total of 2,140 subjects, five groups were identified through k-means clustering, namely putative "near-normal (n=232)," "asthmatic (n=392)," "chronic obstructive pulmonary disease (COPD) (n=37)," "asthmatic-overlap (n=893)," and "COPD-overlap (n=586)" subtypes. Near-normal group showed the oldest mean age (72±7 years) and highest FEV1 (102%±8% predicted), and asthmatic group was the youngest (46±9 years). COPD and COPD-overlap groups were male predominant and all current or ex-smokers. While asthmatic group had the lowest prescription rate despite the highest proportion of self-reported wheezing, COPD, asthmatic-overlap, and COPD-overlap groups showed high prescription rate of respiratory medicine. Although COPD group formed only 1.7% of total subjects, they showed the highest mean medical cost and health care utilization, comprising 5.3% of the total medical cost. When calculating a ratio of total medical expense to household income, the mean ratio was highest in the COPD group. CONCLUSION Clinical and epidemiological heterogeneities of subjects with mild-to-moderate airflow limitation and a different level of health care utilization by each subtype are shown. Identification of a subtype with high health care demand could be a priority for effective utilization of limited resources.