Objective  To investigate the phenotyping of COPD by cluster analysis and evaluate the value of this method.
Methods  168 COPD patients were enrolled from Beijing Tongren Hospital. Demographic and clinical data, such as, sex, age, body mass index ( BMI) , smoking index, course of disease,exacerbation rate, and comorbidities were collected. Pulmonary function test, emphysema scoring by HRCT,dyspnea by MMRC score, COPD assessment test ( CAT) score, six-minute walk test were performed for each patient during the stable stage. Cluster analysis was conducted using SPSS 13. 0.
Results  According to the GOLD criteria,5, 75, 75, and 13 patients were classified into GOLD stage 1, 2, 3, and 4, respectively. There was no difference among different stages in sex distribution, BMI, smoking index, hypertension, and cerebral infarction incidence( P  gt; 0. 05) , but the differences in age, disease course, dyspnea score, six-minute walk distance, BODE score, CAT score, coronary heart disease, exacerbation rate, and HRCT emphysema visual score were significant( P  lt;0. 05) . By cluster analysis,168 patients were finally classified into three groups:younger/mild, older/ severe, and older/moderate. The patients with the same GOLD stage appeared in
different clusters and the patients belonging to different GOLD stages could be in the same cluster. There were significant differences among three groups in age, BMI, exacerbation rate, dyspnea score, CAT score, and comorbidities. The result showed that HRCT emphysema visual score was also an important index to
differentiate clusters, suggesting that emphysema was an important phenotype of COPD.
Conclusions  Cluster analysis can classify homogeneous subjects into the same cluster, and heterogeneous subjects into different clusters. The results suggest that COPD phenotyping by cluster analysis is clinically useful and significant.

Citation： WEI Dandan,BAI Peng,SUN Yongchang. Phenotyping of Chronic Obstructive Pulmonary Disease by Using Cluster Analysis. Chinese Journal of Respiratory and Critical Care Medicine, 2012, 11(5): 417-421. doi: Copy