Risk stratification in anti‐MDA5+ dermatomyositis‐related interstitial lung disease by using cluster analysis

Abstract Background Anti‐MDA5+ dermatomyositis (DM) represents a heterogeneous group of patients as diverse individuals display different clinical characteristics, disease progression and complications development. The heterogeneity makes it difficult to classify interstitial lung disease in anti‐MDA5 antibodies positive dermatomyositis (anti‐MDA5+ DM‐ILD). Objectives To distinguish and characterize phenotypic subgroups for anti‐MDA5+ DM‐ILD patients. Methods From August 2014 to March 2022, 188 anti‐MDA5+ DM‐ILD patients were retrospectively enrolled. 21 HRCT‐derived quantitative features were reduced to four principal components through principal component analysis. The missForest algorithm was employed for missing data imputation. Clusters were determined by partitioning around medoids. The classification and regression tree (CART) algorithm was utilized to distinguish between three clusters. Results The Silhouette Coefficient and Dunn index indicated an optimal cluster number of 3. Patients in cluster 2 (high‐risk cluster) have the highest RP‐ILD incidence rate (84.9% vs. 18.1% vs. 17.1%, cluster 2 vs. 1 vs. 3, p < 0.001), extremely high early mortality (88.7% vs. 4.3% vs. 2.4%, cluster 2 vs. 1 vs. 3, p < 0.001) and moderate dermato‐rheumatologic pattern. Patients in cluster 1 correspond to a pure dermato‐rheumatologic pattern (dermato‐rheumatologic cluster). Cluster 3 (low‐risk cluster) was characterized by not obvious dermato‐rheumatologic and the lowest RP‐ILD incidence (17.1%) as well as the lowest early mortality (2.4%). The accuracy of the CART algorithm in differentiating clusters was 67.7% in the validation cohort with 56 patients. Principal component 1 was a key feature in the CART algorithm. Sensitivity analyses employing multiple clustering approaches confirmed the robustness of the three‐cluster solution by partitioning around medoids. Conclusions Clustering analysis offers valuable insights into the heterogeneity and clinical implications of anti‐MDA5+ DM‐ILD. HRCT‐derived quantitative features demonstrate significant value for early risk stratification in anti‐MDA5+ DM‐ILD.