Journal of Clinical Medicine (Apr 2021)

Prognostic and Clinical Value of Cluster Analysis in Idiopathic Pleuroparenchymal Fibroelastosis Phenotypes

  • Yutaro Nakamura,
  • Kazutaka Mori,
  • Yasunori Enomoto,
  • Masato Kono,
  • Hiromitsu Sumikawa,
  • Takeshi Johkoh,
  • Thomas V. Colby,
  • Hideki Yasui,
  • Hironao Hozumi,
  • Masato Karayama,
  • Yuzo Suzuki,
  • Kazuki Furuhashi,
  • Tomoyuki Fujisawa,
  • Noriyuki Enomoto,
  • Naoki Inui,
  • Yusuke Kaida,
  • Koshi Yokomura,
  • Naoki Koshimizu,
  • Mikio Toyoshima,
  • Shiro Imokawa,
  • Takashi Yamada,
  • Toshihiro Shirai,
  • Hidenori Nakamura,
  • Hiroshi Hayakawa,
  • Takafumi Suda

DOI
https://doi.org/10.3390/jcm10071498
Journal volume & issue
Vol. 10, no. 7
p. 1498

Abstract

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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 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.

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