Lei Wang1, Rui Liang1, Ting Zhou2, Jing Zheng1, Bing Miao Liang3, Hong Ping Zhang1, Feng Ming Luo3, Peter G Gibson4, Gang Wang5. 1. Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, State Key Laboratory of Biotherapy of China, West China Hospital, Sichuan University, Chengdu, People's Republic of China. 2. Health Management Center, West China Hospital, Sichuan University, Chengdu, People's Republic of China. 3. Department of Respiratory and Critical Care Medicine, West Chia Hospital, Sichuan University, Chengdu, People's Republic of China. 4. Department of Respiratory and Sleep Medicine, John Hunter Hospital, Hunter Medical Research Institute, University of Newcastle, Priority Research Centre for Healthy Lungs, New Lambton Heights, New South Wales, Australia. 5. Pneumology Group, Department of Integrated Traditional Chinese and Western Medicine, State Key Laboratory of Biotherapy of China, West China Hospital, Sichuan University, Chengdu, People's Republic of China. Electronic address: wcums-respiration@hotmail.com.
Abstract
BACKGROUND: Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. OBJECTIVE: To identify and prospectively validate asthma clusters in a Chinese population. METHODS: Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. RESULTS: Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons). CONCLUSION: We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries.
BACKGROUND:Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management. OBJECTIVE: To identify and prospectively validate asthma clusters in a Chinese population. METHODS: Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters. RESULTS: Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons). CONCLUSION: We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries.
Authors: Jocelyn S Gandelman; Michael T Byrne; Akshitkumar M Mistry; Hannah G Polikowsky; Kirsten E Diggins; Heidi Chen; Stephanie J Lee; Mukta Arora; Corey Cutler; Mary Flowers; Joseph Pidala; Jonathan M Irish; Madan H Jagasia Journal: Haematologica Date: 2018-09-20 Impact factor: 9.941
Authors: Andy Dahl; Na Cai; Arthur Ko; Markku Laakso; Päivi Pajukanta; Jonathan Flint; Noah Zaitlen Journal: PLoS Genet Date: 2019-04-05 Impact factor: 5.917