Journal
JOURNAL OF CLINICAL MEDICINE
Volume 10, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/jcm10071498
Keywords
idiopathic pleuroparenchymal fibroelastosis; cluster analysis; prognosis
Categories
Funding
- Ministry of Health, Labour and Welfare, Japan
Ask authors/readers for more resources
Idiopathic pleuroparenchymal fibroelastosis (PPFE) is a distinct interstitial pneumonia characterized by unique morphological features, and affected patients can be categorized into four different clinical phenotypes based on cluster analysis, which may help predict survival outcomes. Patients with PPFE in cluster 3, which includes elderly male smokers with a coexisting IP-like pattern, have significantly worse survival outcomes compared to those in clusters 1, 2, and 4.
Idiopathic pleuroparenchymal fibroelastosis (PPFE) is a distinctive interstitial pneumonia with upper lobe predominance that shows unique morphological features among idiopathic interstitial pneumonias (IIPs). Affected patients have a variety of clinical presentations with heterogeneous clinical courses. Cluster analysis is a valuable tool for identifying distinct clinical phenotypes under heterogeneous conditions. This study aimed to identify the phenotypes of patients with idiopathic PPFE. Using cluster analysis, novel PPFE phenotypes were identified among subjects from our multicenter cohort, and outcomes were stratified according to phenotypic clusters. Among the subjects with baseline data (N = 84), four clusters were identified. Cluster 1 included younger male subjects with coexisting non-UIP-like patterns. Cluster 2 included elderly female nonsmokers with low body mass index (BMI). Cluster 3 included elderly male smokers with a coexisting IP-like pattern. Cluster 4 included younger male smokers without lower lobe lesions. Patients in cluster 3 had significantly worse survival outcomes than those in clusters 1, 2, and 4 (p < 0.001, p = 0.0041, and p = 0.0155, respectively). Among idiopathic PPFE patients, cluster analysis using baseline characteristics identified four distinct clinical phenotypes that might predict survival outcomes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available