ArticleLupus science & medicine2026
Prognostic value of baseline clinical features in SLE: a cluster analysis.
Article in Lupus science & medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSLE is a heterogeneous systemic autoimmune disease with diverse clinical manifestations. We aimed to identify clinical subgroups of SLE patients using cluster analysis at diagnosis and at the last follow-up and to evaluate their prognostic implications for disease activity, organ damage and mortality.
methodsIn this single-centre retrospective cohort study, 199 patients with SLE who fulfilled the 1997 American College of Rheumatology (ACR) classification criteria and were regularly followed at the Rheumatology Clinic of Kocaeli University Hospital were included. Patients were divided into clusters based on their clinical involvement at diagnosis and the last visit using the Gower distance and the Partitioning Around Medoids algorithm. Differences between clusters were assessed using one-way analysis of variance, Kruskal-Wallis and χ
resultsAt diagnosis, three clusters were identified: Cluster 1 (n=108) with predominant arthritis; Cluster 2 (n=49) with renal and haematological involvement and highest disease activity (SLE Disease Activity Index 2000, p=0.004); Cluster 3 (n=42) with mucocutaneous manifestations, often accompanied by haematological involvement. After a median follow-up of 168 months, four clusters were identified. Cluster 4', a heterogeneous group with mucocutaneous, articular and renal involvement, had significantly higher Systemic Lupus International Collaborating Clinics (SLICC)/ACR Damage Index (SDI) scores (median 4.0 (0-8), p<0.001), indicating poor prognosis. During follow-up, 29.6% of Cluster 1, 22.4% of Cluster 2 and 42.9% of Cluster 3 transitioned to Cluster 4'. Patients with mucocutaneous-predominant disease at diagnosis had a twofold increased risk of transitioning to the poor-prognosis cluster (OR 2.05, 95% CI 1.01 to 4.16, p=0.046). Mortality did not differ significantly between clusters.
conclusionCluster analysis based on clinical involvement in SLE can identify homogeneous patient subgroups and predict long-term outcomes. Patients with mucocutaneous-predominant onset, often receiving less intensive treatment, may have a worse prognosis, highlighting the importance of individualised monitoring and management strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.