Unknown

Dataset Information

0

Cluster features in fibrosing interstitial lung disease and associations with prognosis.


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.

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10621076 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Cluster features in fibrosing interstitial lung disease and associations with prognosis.

Wang Yuanying Y   Sun Di D   Wang Jingwei J   Yu Shiwen S   Wu Na N   Ye Qiao Q  

BMC pulmonary medicine 20231101 1


<h4>Background</h4>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.<h4>Methods</h4>Retrospectively, 575 out of 926 patients with F-ILD were eligible for analysis. Four clusters were identified based on baseline data using cluster analysis. Th  ...[more]

Similar Datasets

| S-EPMC6988233 | biostudies-literature
| S-EPMC10039317 | biostudies-literature
| S-EPMC8559348 | biostudies-literature
| PRJNA248931 | ENA
| S-EPMC10022771 | biostudies-literature
| S-EPMC6824393 | biostudies-literature
| S-EPMC9748274 | biostudies-literature
| S-EPMC9314965 | biostudies-literature
| S-EPMC8411897 | biostudies-literature
| S-EPMC7467418 | biostudies-literature