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Risk stratification in anti-MDA5+ dermatomyositis-related interstitial lung disease by using cluster analysis.

Wenzhang He1,2, Beibei Cui3, Zhigang Chu4

  • 1Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.

Journal of the European Academy of Dermatology and Venereology : JEADV
|July 18, 2025
PubMed
Summary

This study identified three distinct patient subgroups within anti-MDA5+ DM-ILD, enabling better risk stratification. High-risk patients show higher mortality and interstitial lung disease incidence, guiding personalized treatment strategies.

Keywords:
cluster analysiscomputed tomographydecision‐tree modeldermatomyositisinterstitial lung diseaserisk factor

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Area of Science:

  • Rheumatology
  • Pulmonology
  • Medical Imaging

Background:

  • Anti-MDA5+ dermatomyositis with interstitial lung disease (anti-MDA5+ DM-ILD) is heterogeneous.
  • This heterogeneity complicates classification and treatment strategies for anti-MDA5+ DM-ILD patients.

Purpose of the Study:

  • To identify and characterize distinct phenotypic subgroups within the anti-MDA5+ DM-ILD patient population.
  • To improve early risk stratification and clinical management of anti-MDA5+ DM-ILD.

Main Methods:

  • Retrospective analysis of 188 anti-MDA5+ DM-ILD patients from August 2014 to March 2022.
  • Utilized principal component analysis on 21 HRCT features and partitioning around medoids for clustering.
  • Employed classification and regression tree (CART) algorithm for distinguishing identified clusters.

Main Results:

  • Identified three robust patient clusters using clustering analysis.
  • Cluster 2 (high-risk) exhibited significantly higher rates of rapidly progressive interstitial lung disease (RP-ILD) and early mortality.
  • Cluster 1 represented a pure dermato-rheumatologic pattern, while Cluster 3 (low-risk) showed minimal RP-ILD and low mortality.

Conclusions:

  • Clustering analysis effectively reveals heterogeneity and clinical implications in anti-MDA5+ DM-ILD.
  • HRCT-derived quantitative features are valuable for early risk stratification in anti-MDA5+ DM-ILD patients.