ArticleClinical rheumatology2024
Performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in a cohort of Colombian population.
Article in Clinical rheumatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers 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
- Real-world validation of the SLERPI diagnostic model with concordance and discordance analysis across established SLE classification criteria.Arthritis research & therapy · 2026Article
- Artificial Intelligence in Rheumatology: Quo Vadis?Mediterranean journal of rheumatology · 2025Article
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Abstract
objectiveTo evaluate the performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in Colombian patients with systemic lupus erythematosus (SLE).
methodsThe Colombian cohort included 435 SLE patients and 430 controls with other autoimmune diseases (ADs). Clinical and serological data were collected, and SLE was indicated by SLERPI scores > 7. The American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012, and European League Against Rheumatism (EULAR)/ACR-2019 criteria were used as reference standards. The impact of overt polyautoimmunity (PolyA) on SLERPI performance was assessed. Additionally, multivariate lineal regression analysis was performed to evaluate the contribution of SLERPI features to the overall SLERPI score.
resultsSLE patients had higher SLERPI scores (P < 0.0001), with almost 90% meeting "definite" lupus criteria. Main factors influencing SLERPI included immunological disorder (β:44.75, P < 0.0001), malar/maculopapular rash (β:18.43, P < 0.0001), and anti-nuclear antibody positivity (β:15.65, P < 0.0001). In contrast, subacute cutaneous lupus erythematosus/discoid lupus erythematosus (β:2.40, P > 0.05) and interstitial lung disease (β:-21.58, P > 0.05) were not significant factors to the overall SLERPI score. SLERPI demonstrated high sensitivity for SLE, both for the overall SLE group and for those without overt PolyA (95.4% and 94.6%, respectively), but had relatively low specificity (92.8% and 93.7%, respectively). The model showed high sensitivity for hematological lupus (98.8%) and lupus nephritis (96.0%), but low sensitivity for neuropsychiatric lupus (93.2%). Compared to the ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria, SLERPI yielded the highest sensitivity and lowest specificity.
conclusionSLERPI efficiently identified SLE patients in a Colombian cohort, showing high sensitivity but low specificity. The model effectively distinguishes SLE patients, even in the presence of concurrent overt PolyA. Key Points •SLERPI has a high sensitivity, but low specificity compared to ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria in the Colombian population. •Within the SLERPI score, immunological disorder, malar/maculopapular rash, and anti-nuclear antibody positivity are the strongest predictors of SLE. •SLERPI model can efficiently distinguish patients with SLE, regardless of concomitant overt PolyA. •SLERPI demonstrates high sensitivity in identifying hematological and nephritic subphenotypes of SLE.
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