Identification of Three Different Phenotypes in Anti–Melanoma Differentiation–Associated Gene 5 Antibody–Positive Dermatomyositis Patients: Implications for Prediction of Rapidly Progressive Interstitial Lung Disease. Issue 4 (17th March 2023)

• Record Type: Journal Article
• Authors: Xu, Lingxiao; You, Hanxiao; Wang, Lei; Lv, Chengyin; Yuan, Fenghong; Li, Ju; Wu, Min; Da, Zhanyun; Wei, Hua; Yan, Wei; Zhou, Lei; Yin, Songlou; Zhou, Dongmei; Wu, Jian; Lu, Yan; Su, Dinglei; Liu, Zhichun; Liu, Lin; Ma, Longxin; Xu, Xiaoyan; Zang, Yinshan; Liu, Huijie; Ren, Tianli; Wang, Fang; Du, Yan; Xue, Jing; Zhang, Miaojia; Tan, Wenfeng
• Is Part Of: Arthritis & rheumatology. Volume 75:Issue 4(2023)
• Page Start: 609
• Page End: 619
• Publication Date: 2023-03-17
• Subjects: Arthritis -- Periodicals; Rheumatism -- Periodicals; 616.72
• DOI: 10.1002/art.42308
• Languages: English
• ISSNs: 2326-5191

Abstract

Abstract : Objective: There is substantial heterogeneity among the phenotypes of patients with anti–melanoma differentiation–associated gene 5 antibody–positive (anti‐MDA5+) dermatomyositis (DM), hindering disease assessment and management. This study aimed to identify distinct phenotype groups in patients with anti‐MDA5+ DM and to determine the utility of these phenotypes in predicting patient outcomes. Methods: A total of 265 patients with anti‐MDA5+ DM were retrospectively enrolled in the study. An unsupervised hierarchical cluster analysis was performed to characterize the different phenotypes. Results: Patients were stratified into 3 clusters characterized by markedly different features and outcomes. Cluster 1 (n = 108 patients) was characterized by mild risk of rapidly progressive interstitial lung disease (RPILD), with the cumulative incidence of non‐RPILD being 85.2%. Cluster 2 (n = 72 patients) was characterized by moderate risk of RPILD, with the cumulative incidence of non‐RPILPD being 73.6%. Patients in cluster 3 (n = 85 patients), which was characterized by a high risk of RPILD and a cumulative non‐RPILD incidence of 32.9%, were more likely than patients in the other 2 subgroups to have anti–Ro 52 antibodies in conjunction with high titers of anti‐MDA5 antibodies. All‐cause mortality rates of 60%, 9.7%, and 3.7% were determined for clusters 3, 2, and 1, respectively ( P < 0.0001). Decision tree analysis led to the development of a simple algorithm for anti‐MDA5+ DM patient classification that included the following 8 variables: age >50 years, disease course of <3 months, myasthenia (proximal muscle weakness), arthritis, C‐reactive protein level, creatine kinase level, anti–Ro 52 antibody titer, and anti‐MDA5 antibody titer. This algorithm placed patients in the appropriate cluster with 78.5% accuracy in the development cohort and 70.0% accuracy in the external validation cohort. Conclusion: Cluster analysis identified 3 distinct clinical patterns and outcomes in our large cohort of anti‐MDA5+ DM patients. Classification of DM patients into phenotype subgroups with prognostic values may help physicians improve the efficacy of clinical decision‐making.