# A new proposal for phenotypic classification and outcome assessment of dermatomyositis based on clinical manifestations and serological testing

## TL;DR

This paper proposes a new way to classify dermatomyositis patients into three groups based on clinical and serological features to better predict outcomes and guide treatment.

## Contribution

The study introduces a novel phenotypic classification of dermatomyositis using unsupervised clustering and clinical-serological data.

## Key findings

- Three distinct patient clusters were identified with varying risks of poor outcomes.
- Cluster 2 had the highest mortality rate and included patients with anti-MDA5+ and RP-ILD.
- The classification helps in evaluating patients and enabling personalized care.

## Abstract

Dermatomyositis (DM) is an infrequent disease subgroup of idiopathic inflammatory myopathies characterized by distinct skin lesions. However, high heterogeneity makes clinical diagnosis and treatment of DM very challenging.

Unsupervised classification in DM patients and analysis of key factors related to clinical outcomes.

This retrospective study was conducted between 2017 and 2022 at the Department of Rheumatology, Xiangya Hospital, Central South University. 162 DM patients were enrolled for unsupervised hierarchical cluster analysis. In addition, we divided the clinical outcomes of DM patients into four subgroups: withdrawal, stabilization, aggravation, and death, and compared the clinical profiles amongst the subgroups.

Out of 162 DM patients, three clusters were defined. Cluster 1 (n = 40) was mainly grouped by patients with prominent muscular involvement and mild Interstitial Lung Disease (ILD). Cluster 2 (n = 72) grouped patients with skin rash, anti-Melanoma Differentiation Associated protein 5 positive (anti-MDA5+), and Rapid Progressive Interstitial Lung Disease (RP-ILD). Cluster 3 (n = 50) grouped patients with the mildest symptoms. The proportion of death increased across the three clusters (cluster 3 < cluster 1 < cluster 2).

The number of cases was limited for the subsequent construction and validation of predictive models. We did not review all skin symptoms or pathological changes in detail.

We reclassified DM into three clusters with different risks for poor outcome based on diverse clinical profiles. Clinical serological testing and cluster analysis are necessary to help clinicians evaluate patients during follow-up and conduct phenotype-based personalized care in DM.