ArticleRMD open2025
Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage.
Article in RMD open, 2025. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Comparative evaluation of large language models on multiple-choice and image-based rheumatology questions.Rheumatology international · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOrgan damage is a key determinant of poor prognosis and increased mortality in systemic lupus erythematosus (SLE). However, no validated clinical tools for predicting damage accumulation currently exist. We sought to develop a machine learning-based model to predict early organ damage in patients with SLE.
methodsClassification criteria (American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012, European League Against Rheumatism (EULAR)/ACR-2019) and non-criteria features of a cohort of 914 patients with SLE were analysed to predict damage (defined as SLICC/ACR Damage Index (SDI)) within 5 years since diagnosis. Feature selection and model construction were performed using the least absolute shrinkage and selection operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in an external cohort (n=50).
resultsA LASSO-LR model incorporating 16 criteria and non-criteria features predicted early organ damage with area under the receiver operating characteristic curve (AUC) values of 0.80 (95% CI 0.74 to 0.87) and 0.86 (95% CI 0.76 to 0.97) in the derivation and external validation cohorts, respectively, outperforming the ACR-1997 (AUC 0.70; 95% CI 0.62 to 0.78), SLICC-2012 (AUC 0.74; 95% CI 0.66 to 0.81) and EULAR/ACR-2019 (AUC 0.70; 95% CI 0.63 to 0.78) classification systems. Features most strongly associated with damage included the SLICC-2012 neurological disorder, EULAR/ACR-2019 class III/IV lupus nephritis and among non-criteria manifestations, myocarditis and interstitial lung disease. Operating the model as a binary classifier (early damage versus no damage), it demonstrated high specificity (0.90, 95% CI 0.78 to 0.95). The model can be converted to a simplified scoring system, with a threshold of ≥3 achieving an AUC of 0.86 (95% CI 0.75 to 0.96).
conclusionWe developed and validated a clinician-friendly model for early organ damage prediction in SLE, facilitating risk stratification.
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