Cluster features in fibrosing interstitial lung disease and associations with prognosis

• Published in: BMC pulmonary medicine Vol. 23; no. 1; pp. 1 - 420
• Main Authors: Wang, Yuanying, Sun, Di, Wang, Jingwei, Yu, Shiwen, Wu, Na, Ye, Qiao
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 01.11.2023; BioMed Central; BMC
• Subjects: Acute exacerbation; Analysis; Cardiovascular disease; Care and treatment; Cluster analysis; Comorbidity; Development and progression; Fibrosis; Hypoxemia; Interstitial lung diseases; Lung diseases; Lung diseases, Interstitial; Methods; Mortality; Occupational exposure; Patients; Prognosis; Pulmonary fibrosis; Pulmonary function tests; Pulmonology; Respiratory function; Smoking; Survival; China; Beijing China
• ISSN: 1471-2466; 1471-2466
• DOI: 10.1186/s12890-023-02735-7

Abstract Background Clustering is helpful in identifying subtypes in complex fibrosing interstitial lung disease (F-ILD) and associating them with prognosis at an early stage of the disease to improve treatment management. We aimed to identify associations between clinical characteristics and outcomes in patients with F-ILD. Methods Retrospectively, 575 out of 926 patients with F-ILD were eligible for analysis. Four clusters were identified based on baseline data using cluster analysis. The clinical characteristics and outcomes were compared among the groups. Results Cluster 1 was characterized by a high prevalence of comorbidities and hypoxemia at rest, with the worst lung function at baseline; Cluster 2 by young female patients with less or no smoking history; Cluster 3 by male patients with highest smoking history, the most noticeable signs of velcro crackles and clubbing of fingers, and the severe lung involvement on chest image; Cluster 4 by male patients with a high percentage of occupational or environmental exposure. Clusters 1 (median overall survival [OS] = 7.0 years) and 3 (OS = 5.9 years) had shorter OS than Clusters 2 (OS = not reached, Cluster 1: p < 0.001, Cluster 3: p < 0.001) and 4 (OS = not reached, Cluster 1: p = 0.004, Cluster 3: p < 0.001). Clusters 1 and 3 had a higher cumulative incidence of acute exacerbation than Clusters 2 (Cluster 1: p < 0.001, Cluster 3: p = 0.014) and 4 (Cluster 1: p < 0.001, Cluster 3: p = 0.006). Stratification by using clusters also independently predicted acute exacerbation (p < 0.001) and overall survival (p < 0.001). Conclusions The high degree of disease heterogeneity of F-ILD can be underscored by four clusters based on clinical characteristics, which may be helpful in predicting the risk of fibrosis progression, acute exacerbation and overall survival.