title: Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.
abstract: BACKGROUND
  Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.
  
  
  OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Identification of distinct phenotypes will lead to greater insight into the characteristics and prognosis of bronchiectasis.
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
authors.forename: W-J
authors.initials: WJ
authors.lastname: Guan
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
authors.forename: M
authors.initials: M
authors.lastname: Jiang
authors.affiliation: Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
authors.forename: Y-H
authors.initials: YH
authors.lastname: Gao
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
authors.forename: H-M
authors.initials: HM
authors.lastname: Li
authors.affiliation: Guangzhou First People's Hospital, Guangzhou, Guangdong, China.
authors.forename: G
authors.initials: G
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
authors.forename: J-P
authors.initials: JP
authors.lastname: Zheng
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, China. chenrc@vip.163.com.
authors.forename: R-C
authors.initials: RC
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, China. nanshan@vip.163.com.
authors.forename: N-S
authors.initials: NS
authors.lastname: Zhong
mesh_terms: D000328:Adult; D000368:Aged; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D015992:Body Mass Index; D001987:Bronchiectasis / Q000175:diagnosis* / Q000188:drug therapy / Q000235:genetics*; D016000:Cluster Analysis; D015331:Cohort Studies; D003371:Cough; D005260:Female; D005500:Follow-Up Studies; D005541:Forced Expiratory Volume; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D011446:Prospective Studies; D011550:Pseudomonas aeruginosa / Q000302:isolation & purification; D011788:Quality of Life; D012307:Risk Factors; D013183:Sputum / Q000382:microbiology; D000069558:Unsupervised Machine Learning
chemical_list: D000900:Anti-Bacterial Agents