Prediction of first acute exacerbation using COPD subtypes identified by cluster analysis - Yoon HY; Park SY; Lee CH; Byun MK; Na JO; Lee JS; Lee WY; Yoo KH; Jung KS; Lee JH (International Journal of COPD, 2019)
Hee-Young Yoon,1 So Young Park,1 Chang Hoon Lee,2 Min-Kwang Byun,3 Joo Ock Na,4 Jae Seung Lee,5 Won-Yeon Lee,6 Kwang Ha Yoo,7 Ki-Suck Jung,8 Jin Hwa Lee11Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Ewha Womans Seoul Hospital, Ewha Womans University, Seoul, Korea; 2Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea; 3Division of Pulmonary Medicine, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Korea; 4Division of Pulmonology, Department of Internal Medicine, Soonchunhyang University, College of Medicine, Cheonan, Korea; 5Department of Pulmonary and Critical Care Medicine, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Korea; 6Department of Internal Medicine, Wonju Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, Korea; 7Department of Internal Medicine, Konkuk University College of Medicine, Seoul, Korea; 8Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Medical Center, Hallym University College of Medicine, Anyang, KoreaPurpose: In patients with COPD, acute exacerbation (AE) is not only an important determinant of prognosis, but also an important factor in choosing therapeutic agents. In this study, we evaluated the usefulness of COPD subtypes identified through cluster analysis to predict the first AE. Patients and methods: Among COPD patients in the Korea COPD Subgroup Study (KOCOSS) cohort, 1,195 who had follow-up data for AE were included in our study. We selected seven variables for cluster analysis – age, body mass index, smoking status, history of asthma, COPD assessment test (CAT) score, post-bronchodilator (BD) FEV1 % predicted, and diffusing capacity of carbon monoxide % predicted. Results: K-means clustering identified four clusters for COPD that we named putative asthma-COPD overlap (ACO), mild COPD, moderate COPD, and severe COPD subtypes. The ACO group (n=196) showed the second-best post-BD FEV1 (75.5% vs 80.9% [mild COPD, n=313] vs 52.4% [moderate COPD, n=345] vs 46.7% [severe COPD, n=341] predicted), the longest 6-min walking distance (424 m vs 405 m vs 389  m vs 365 m), and the lowest CAT score (12.2 vs 13.7 vs 15.6 vs 17.5) among the four groups. ACO group had greater risk for first AE compared to the mild COPD group (HR, 1.683; 95% CI, 1.175–2.410). The moderate COPD and severe COPD group HR values were 1.587 (95% CI, 1.145–2.200) and 1.664 (95% CI, 1.203–2.302), respectively. In addition, St. George’s Respiratory Questionnaire score (HR: 1.019; 95% CI, 1.014–1.024) and gastroesophageal reflux disease were independent factors associated with the first AE (HR: 1.535; 95% CI, 1.116–2.112). Conclusion: Our cluster analysis revealed an exacerbator subtype of COPD independent of FEV1. Since these patients are susceptible to AE, a more aggressive treatment strategy is needed in these patients.Keywords: clustering, prognosis, phenotype, asthma-COPD overlap, exacerbation, comorbidity