Dataset Information


Identification of 6 dermatomyositis subgroups using principal component analysis-based cluster analysis.

ABSTRACT: OBJECTIVE:Dermatomyositis (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. METHODS:We 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. RESULTS:We 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. CONCLUSION:We 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.


PROVIDER: S-EPMC6771972 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

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