Identification of clinical subgroups in anti-SRP positive immune-mediated necrotizing myopathy patients using cluster analysis - Beibei Cui; Hongjiang Liu; Ruiting Liu; Geng Yin; Qibing Xie (Therapeutic Advances in Musculoskeletal Disease)
Background: Anti-signal recognition particle immune-mediated necrotizing myopathy (anti-SRP IMNM) is a rare autoimmune disorder characterized by muscle weakness and necrosis. Identifying clinical subgroups within this patient population could facilitate the management of the disease. Objectives: To identify distinct clinical subgroups of anti-SRP IMNM patients. Design: A retrospective study was conducted on anti-SRP IMNM patients treated at West China Hospital of Sichuan University between January 2010 and October 2023. Methods: Clinical data were collected. Unsupervised cluster analysis was conducted to classify patients into distinct subgroups based on their clinical features. Statistical analyses were performed to compare the clinical characteristics and outcomes among the identified clusters. Results: A total of 116 patients were included in the study, and 3 distinct clinical subgroups were identified: Cluster 3 (acute), Cluster 2 (subacute), and Cluster 1 (poor prognosis). Patients in Cluster 3 exhibited a short disease course (median 3 months), severe muscle weakness (78.38% with Medical Research Council (MRC) score ⩽3), high muscle enzyme levels, and a good response to treatment. Cluster 2 patients were younger (mean age 45.83 years), had a longer disease course (median 6.5 months), milder muscle damage, and lower autoantibody titers. Cluster 1 patients were older (mean age 58.10 years), predominantly male (70.97%), and had higher incidences of interstitial lung disease (70.97%) and cardiac injury (45.16%). In Cluster 1, 16.13% of cases were refractory, and the relapse rate was 38.71%, which was significantly higher compared to the other two clusters. Conclusion: This study highlights the clinical heterogeneity among anti-SRP IMNM patients and identifies three distinct clinical subgroups with unique characteristics. These findings provide insights for personalized management.