So Young Park1, Hee Won Jung2, Jae Moon Lee3, Bomi Shin3, Hyo Jung Kim3, Min-Hye Kim4, Woo-Jung Song3, Hyouk-Soo Kwon3, Jae-Woo Jung5, Sae-Hoon Kim6, Heung-Woo Park7, An-Soo Jang8, Yoon-Seok Chang6, You Sook Cho3, Young-Joo Cho4, Sang-Heon Cho7, Byoung Whui Choi5, Sungho Won9, Taesung Park10, Hee-Bom Moon3, Changsoo Kim11, Tae-Bum Kim12. 1. Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; Department of Internal Medicine, Eulji University School of Medicine, Seoul, Korea. 2. Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea; Department of Applied Statistic, Chung-Ang Graduate University, Seoul, Korea. 3. Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. 4. Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Korea. 5. Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Korea. 6. Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea. 7. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. 8. Department of Internal Medicine, Soonchunhyang University College of Medicine, Bucheon, Korea. 9. Department of Public Health Sciences, Seoul National University, Seoul, Korea; Institute of Health and Environment, Seoul National University, Seoul, Korea. 10. Department of Statistics, Seoul National University, Seoul, Korea. 11. Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea. 12. Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. Electronic address: tbkim@amc.seoul.kr.
Abstract
BACKGROUND: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. OBJECTIVE: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. METHODS: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. RESULTS: Two main sets of longitudinal trajectories were identified in terms of FEV1% predicted, and FEV1 variability. In the 4 trajectories determined with FEV1% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. CONCLUSIONS: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.
BACKGROUND: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. OBJECTIVE: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. METHODS: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. RESULTS: Two main sets of longitudinal trajectories were identified in terms of FEV1% predicted, and FEV1 variability. In the 4 trajectories determined with FEV1% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. CONCLUSIONS: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.
Authors: Minna Tommola; Ha-Kyeong Won; Pinja Ilmarinen; Heewon Jung; Leena E Tuomisto; Lauri Lehtimäki; Onni Niemelä; Tae-Bum Kim; Hannu Kankaanranta Journal: Respir Res Date: 2020-07-13