ArticleArthritis research & therapy2026
Real-world validation of the SLERPI diagnostic model with concordance and discordance analysis across established SLE classification criteria.
Article in Arthritis research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Application of the SLERPI scoring system as a classification tool in systemic lupus erythematosus and its performance in early disease recognition.Rheumatology international · 2026Article
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11 authors.
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Abstract
objectiveSystemic lupus erythematosus (SLE) is a clinically heterogeneous disease in which early and atypical presentations frequently challenge existing classification frameworks. The Systemic Lupus Erythematosus Risk Probability Index (SLERPI) was developed as a probabilistic diagnostic aid, but its real-world performance relative to established classification criteria across disease phenotypes remains incompletely characterized.
methodsIn this multicenter, cross-sectional study, we evaluated 1,281 participants, including 655 expert-confirmed SLE patients and 626 controls with other rheumatic diseases. Diagnostic performance of SLERPI, ACR-1997, SLICC-2012, and EULAR/ACR-2019 criteria was assessed against expert clinical diagnosis as the reference standard. Subgroup analyses were performed for early disease (≤ 1 year), sex, disease duration, and major organ involvement. Concordance and discordance between criteria were examined using UpSet plots, detailed phenotypic comparisons, and hierarchical cluster analysis of discordant cases. Net reclassification improvement (NRI) was used to quantify incremental diagnostic information.
resultsAll four systems demonstrated high diagnostic accuracy, with sensitivities ranging from 95.1-99.2% and specificities from 87.7-90.4%. SLERPI achieved the highest sensitivity (99.2%) and AUC (0.989), particularly excelling in early disease (≤ 1 year, sensitivity 98.0%, AUC 0.987). Net reclassification improvement favored SLERPI over ACR-1997 (+ 2.7%), SLICC-2012 (+ 1.6%), and EULAR/ACR-2019 (+ 4.3%). Concordance across systems was substantial, with 91.6% of patients classified by all four sets. Discordant cases (8.4%) revealed phenotype-specific patterns: ACR-1997 frequently missed immunologically active or hematologic-dominant cases, while EULAR/ACR-2019 underperformed in mucocutaneous-predominant disease. Cluster analysis identified four coherent subgroups, underscoring heterogeneity in missed classifications. SLERPI showed the lowest discordance, with residual misclassifications confined to hematologic-dominant phenotypes.
conclusionSLE classification frameworks show substantial overlap in real-world practice, with discordance driven by phenotype-specific prioritization of disease domains rather than random failure. SLERPI complements established classification criteria by supporting identification of early and atypical SLE presentations, while traditional criteria remain essential for research standardization. Integrating probabilistic diagnostic tools with classification frameworks may enhance SLE recognition across diverse clinical contexts.
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