AI Summary & Key Takeaways
AI InsightClustering analysis might help to better manage and predict disease complications in heterogenous diseases like lupus.
📌 3 Key Takeaways
Abstract
Objective: Systemic lupus erythematosus (SLE) is a complex heterogenous autoimmune disease that can affect multiple organs. We performed clinical clustering analysis to describe a lupus cohort from the University of Pittsburgh Medical Center. Methods: A total of 724 patients who met the American College of Rheumatology (ACR) classification criteria for SLE were included in this study. Clustering was performed using the ACR classification criteria and the partitioning around medoid method. Correlation analysis was performed using the Spearman’s Rho test. Results: Patients with SLE in our cohort identify three district clinical disease subsets. Patients in cluster 1 were significantly more likely to develop renal and hematologic involvement, and had overrepresentation in African–American and male lupus patients. Clusters 2 and 3 identified a milder disease, with a significantly less likelihood of organ complications. Patients in cluster 2 are characterized by malar rash and photosensitivity, while patients in cluster 3 are characterized by oral ulcers, which is present in ~90% of patients within this cluster. The presence of photosensitivity or oral ulcers appears to be protective against the development of lupus nephritis in our cohort. Conclusion: We describe a large cohort of SLE from Western Pennsylvania and identify three distinct clinical disease subgroups. Clustering analysis might help to better manage and predict disease complications in heterogenous diseases like lupus.