Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics

• Published in: The international journal of tuberculosis and lung disease Vol. 20; no. 3; pp. 402 - 410
• Main Authors: Guan, W-J., Jiang, M., Gao, Y-H., Li, H-M., Xu, G., Zheng, J-P., Chen, R-C., Zhong, N-S.
• Format: Journal Article
• Language: English
• Published: France International Union Against Tuberculosis and Lung Disease 01.03.2016
• Subjects: Adult; Aged; Anti-Bacterial Agents - therapeutic use; Body Mass Index; Bronchiectasis; Bronchiectasis - diagnosis; Bronchiectasis - drug therapy; Bronchiectasis - genetics; Cluster; Cluster Analysis; Cohort Studies; Cough; Factor Analysis; Female; Follow-Up Studies; Forced Expiratory Volume; Humans; Male; Middle Aged; Phenotype; Prospective Studies; Pseudomonas aeruginosa - isolation & purification; Quality of Life; Risk Factors; Sputum - microbiology; Unsupervised Learning Technique; Unsupervised Machine Learning
• ISSN: 1027-3719; 1815-7920
• DOI: 10.5588/ijtld.15.0500

BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.OBJECTIVES: To identify bronchiectasis phenotypes and characterise their clinical manifestations and prognosis.METHODS: We conducted hierarchical cluster analysis to identify clusters that best distinguished clinical characteristics of bronchiectasis. Demographics, lung function, sputum bacteriology, aetiology, radiology, disease severity, quality-of-life, cough scale and capsaicin sensitivity, exercise tolerance, health care use and frequency of exacerbations were compared.RESULTS: Data from 148 adults with stable bronchiectasis were analysed. Four clusters were identified. Cluster 1 (n = 69) consisted of the youngest patients with predominantly mild and idiopathic bronchiectasis with minor health care resource use. Patients in cluster 2 (n = 22), in which post-infectious bronchiectasis predominated, had the longest duration of symptoms, greater disease severity, poorer lung function, airway Pseudomonas aeruginosa colonisation and frequent health care resource use. Cluster 3 (n = 16) consisted of elderly patients with shorter duration of symptoms and mostly idiopathic bronchiectasis, and predominantly severe bronchiectasis. Cluster 4 (n = 41) constituted the most elderly patients with moderate disease severity. Clusters 2 and 3 tended to have a greater risk of bronchiectasis exacerbations (P = 0.06) than clusters 1 and 4.CONCLUSION: Identification of distinct phenotypes will lead to greater insight into the characteristics and prognosis of bronchiectasis.