ArticleInternational journal of rheumatic diseases2019
Identification of 6 dermatomyositis subgroups using principal component analysis-based cluster analysis.
Article in International journal of rheumatic diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A multicentre study for clinical phenotype prediction in juvenile dermatomyositis: categorical principal component analysis-based hierarchical clustering.Reumatologia · 2026Article
- Epigenetic patient stratification via contrastive machine learning refines hallmark biomarkers in minoritized children with asthma.Research square · 2024Article
- Identification of two hidden clinical subgroups among men with idiopathic cryptozoospermia.Human reproduction (Oxford, England) · 2024Article
- Identification of novel clinical subtypes in patients with microscopic polyangiitis using cluster analysis: multicenter REVEAL cohort study.Frontiers in immunology · 2024Article
- Clinical Implication of the Relationship between Antimicrobial Resistance and Infection Control Activities in Japanese Hospitals: A Principal Component Analysis-Based Cluster Analysis.Antibiotics (Basel, Switzerland) · 2022Article
- Identification of 6 dermatomyositis subgroups using principal component analysis-based cluster analysis.International journal of rheumatic diseases · 2019Article
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9 authors.
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Abstract
objectiveDermatomyositis (DM) is a heterogeneous disease with a wide range of clinical manifestations. The aim of the present study was to identify the clinical subtypes of DM by applying cluster analysis.
methodsWe retrospectively reviewed the medical records of 720 DM patients and selected 21 variables for analysis, including clinical characteristics, laboratory findings, and comorbidities. Principal component analysis (PCA) was first conducted to transform the 21 variables into independent principal components. Patient classification was then performed using cluster analysis based on the PCA-transformed data. The relationships among the clinical variables were also assessed.
resultsWe transformed the 21 clinical variables into nine independent principal components by PCA and identified six distinct subgroups. Cluster A was composed of two sub-clusters of patients with classical DM and classical DM with minimal organ involvement. Cluster B patients were older and had malignancies. Cluster C was characterized by interstitial lung disease (ILD), skin ulcers, and minimal muscle involvement. Cluster D included patients with prominent lung, muscle, and skin involvement. Cluster E contained DM patients with other connective tissue diseases. Cluster F included all patients with myocarditis and prominent myositis and ILD. We found significant differences in treatment across the six clusters, with clusters E, C and D being more likely to receive aggressive immunosuppressive therapy.
conclusionWe applied cluster analysis to a large group of DM patients and identified 6 clinical subgroups, underscoring the need for better phenotypic characterization to help develop individualized treatments and improve prognosis.
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