Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage

Garantziotis P, Nikolopoulos D, Katechis S, Temiz SA, Nöthling DM, Adamichou C, Bergmann C, Sidiropoulos P, Schett G, Fanouriakis A, Boumpas DT, Bertsias G (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Book Volume: 11

Article Number: e006009

Journal Issue: 3

DOI: 10.1136/rmdopen-2025-006009

Abstract

Objective Organ 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. Methods Classification 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). Results A 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). Conclusion We developed and validated a clinician-friendly model for early organ damage prediction in SLE, facilitating risk stratification.

Authors with CRIS profile

Involved external institutions

Karolinska University Hospital / Karolinska Universitetssjukhuset Sweden (SE) Attikon Hospital / Πανεπιστημιακού Γενικού Νοσοκομείου "Αττικόν" Greece (GR) Hippokration General Hospital of Thessaloniki / Ιπποκράτειο Νοσοκομείο Θεσσαλονίκης Greece (GR) University of Crete / Πανεπιστήμιο Κρήτης Greece (GR)

How to cite

APA:

Garantziotis, P., Nikolopoulos, D., Katechis, S., Temiz, S.A., Nöthling, D.-M., Adamichou, C.,... Bertsias, G. (2025). Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage. RMD Open, 11(3). https://doi.org/10.1136/rmdopen-2025-006009

MLA:

Garantziotis, Panagiotis, et al. "Systemic lupus erythematosus damage risk index (SLE-DRI): a simple machine learning-based tool for identifying patients at risk for early organ damage." RMD Open 11.3 (2025).

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