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dc.contributor.authorRua-Figueroa, Iñigo
dc.contributor.authorPérez-Veiga, Natalia
dc.contributor.authorRodríguez-Almaraz, Esther
dc.contributor.authorGalindo-Izquierdo, María
dc.contributor.authorErausquin, Celia
dc.contributor.authorFernández-Nebro, Antonio
dc.contributor.authorUriarte Itzazelaia, Esther
dc.contributor.authorSerrano-Benavente, Belén
dc.contributor.authorCalvo Alén, Jaime
dc.contributor.authorManrique-Arija, Sara
dc.contributor.authorSenabre, José M.
dc.contributor.authorBernal, José A.
dc.contributor.authorNarváez, Javier
dc.contributor.authorTomero, Eva
dc.contributor.authorAurrecoechea, Elena
dc.contributor.authorIbáñez-Barceló, Mónica
dc.contributor.authorTorrente Segarra, Vicente
dc.contributor.authorSangüesa, Clara
dc.contributor.authorMartínez Taboada, Víctor Manuel 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2025-10-10T09:04:59Z
dc.date.available2025-10-10T09:04:59Z
dc.date.issued2025
dc.identifier.issn2053-8790
dc.identifier.urihttps://hdl.handle.net/10902/37728
dc.description.abstractObjective: Patients with SLE have a well-known increased risk of major comorbidities, although they are also very heterogeneous in terms of the prevalence of comorbid conditions. The relationships of such comorbidities with the outcomes and the severity of index diseases are less known. We aimed to evaluate the interactions between comorbid conditions, in a large multicentre SLE cohort, and their impact on severity and outcomes, using a cluster analysis. Methods: Data on 14 cumulative comorbidities were derived from patients with SLE (American College of Rheumatology (ACR)-97 criteria) who had been included in the retrospective phase of the RELESSER (Spanish Society of Rheumatology National Register of SLE). The Severity Katz Index and the SLICC/ACR Damage Index were calculated. Unsupervised cluster analysis was performed to better characterise the relationships between comorbidities in a large multicentre cohort of patients with SLE. For intercluster differences testing, analysis of variance and Tukey tests were used to compare continuous numerical variables; a Kruskal-Wallis test to discrete variables and the ?² (or Fisher's exact test) were used for categorical ones. Results: A total of 3658 patients with SLE were included. Men accounted for 9.6% of patients. The mean (SD) age was 45.9 years, and 93% were Caucasian. Four clusters, with markedly different comorbidity profiles and outcomes, were identified: in cluster 2 (n=516), patients were grouped around depression (100% of the cases); in cluster 3 (n=418) around serious infections (100%); and in cluster 4 (n=388) around cardiovascular events (also 100%). However, in cluster 1, the largest one (n=2336), no patient had any of the three defining comorbidities of the other clusters, and this cluster was associated with the best outcomes. Conclusions: Cluster analysis identifies well-differentiated subsets of patients with SLE in terms of their comorbidities. The most relevant comorbidities in SLE tend to aggregate in the most severe patient subsets.es_ES
dc.format.extent7 p.es_ES
dc.language.isoenges_ES
dc.publisherBMJ Publishing Groupes_ES
dc.rights© Author(s)2025. Re-use permitted under CC BY-NC.es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.sourceLupus Science and Medicine, 2025, 12(2), e001633es_ES
dc.titleComorbidity clusters and their relationship with severity and outcomes of index diseases, in a large multicentre systemic lupus erythematosus cohortes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1136/lupus-2025-001633es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1136/lupus-2025-001633
dc.type.versionpublishedVersiones_ES


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© Author(s)2025. Re-use permitted under CC BY-NC.Excepto si se señala otra cosa, la licencia del ítem se describe como © Author(s)2025. Re-use permitted under CC BY-NC.