Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

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.