Annals of the rheumatic diseases
July 3, 2026 · via Europe PMC
A data-driven, weighted flare score that accurately predicts organ damage in systemic lupus erythematosus.
Nikolopoulos D, Pitsigavdaki S, Nöthling DM, +14 more, Bertsias G.
AI summary — for doctors, generated from the abstract only
A weighted SLE flare score identified high-risk flares, 40% of episodes, that most strongly predicted later organ damage.
What was studied
A multicentre longitudinal cohort of 354 SLE patients (median follow-up 66 months), with validation in 110 patients.
Key findings
- Both mild/moderate and severe flares raised 12-month damage risk (IRR 1.42 and 1.54)
- Major organ involvement needing high-dose glucocorticoids weighted highest (6), above isolated glucocorticoid escalation (1.5-3) and minor organ involvement or immunosuppressant addition (1 each)
- A threshold of 3.5 or more identified high-risk flares with IRR 2.49 for damage and outperformed the original SFI in the validation cohort
Clinical relevance
It may help prioritise which flares need aggressive prevention of damage.
Who should care
Limitations
Derived in selected cohorts and not yet used prospectively to guide therapy.
Treat flares involving major organs needing high-dose steroids as the highest damage risk.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Objectives
Flares are key determinants of outcome in systemic lupus erythematosus (SLE), yet the conventional mild/moderate vs severe classification inadequately reflects prognosis. We sought to refine flare classification to improve damage prediction and develop a data-driven flare score.
Methods
We analysed a multicentre longitudinal cohort of 354 patients with active SLE requiring treatment at inclusion (median follow-up 66 months). Flares were adjudicated using a modified Safety of Estrogens in Lupus Erythematosus National Assessment-SLE Disease Activity Index Flare Index (SFI). Random Forests and modified flare definitions assessed the contribution of individual SFI variables to subsequent damage. We derived a flare score using bootstrap-validated mixed-effects models. This was validated in an independent cohort (n = 110).
Results
Both mild/moderate and severe SFI flares contributed to elevated 12-month damage risk (incidence rate ratio [IRR]:1.42 and 1.54, respectively). Among SFI subcriteria, major organ involvement requiring high-dose glucocorticoids (GCs) ranked highest for damage prediction (weight = 6), followed by the isolated GCs escalation criteria (weights = 1.5-3), whereas minor organ involvement and immunosuppressants/biologics addition carried lower weights (1 each). A flare score based on these subcriteria demonstrated good predictive performance (Brier score = 0.07). A threshold of ≥ 3.5 identified a subset of high-risk flares (40% of all episodes) that displayed the strongest association with subsequent damage (IRR: 2.49, 95% CI: 1.51-4.10) and outperformed the original SFI definition in the validation cohort (area under the curve-receiver operating characteristic: 0.893 vs 0.816). Although both Definitions of Remission in SLE (DORIS) and lupus low disease activity state correlated with lower risk of SFI-defined flares, only sustained DORIS (>50% of time) reduced high-risk flares.
Conclusions
We defined and validated a novel weighted SLE flare score that better predicts damage accrual and may support treat-to-target implementation and clinical trial design.
DOI
10.1016/j.ard.2026.06.013
PMID
42399124
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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