Comorbidity clusters and their relationship with severity and outcomes of index diseases, in a large multicentre systemic lupus erythematosus cohort.
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Rua-Figueroa Iñigo, Pérez-Veiga Natalia, Rodríguez-Almaraz Esther, Galindo-Izquierdo María, Erausquin Celia, Fernandez-Nebro Antonio, Uriarte Itzazelaia Esther, Serrano-Benavente Belén, Calvo Alén Jaime, Manrique-Arija Sara, Senabre Jose M, Bernal Jose A, Narvaez Javier, Tomero Eva, Aurrecoechea Elena, Ibáñez-Barceló Mónica, Segarra Vicente Torrente, Sangüesa Clara, Freire-González Mercedes, García-Villanueva María Jesús, Martínez Taboada Víctor, Arevalo Marta, Morales Claudia Moriano, Iñiguez Carlota, Perez Ana, Salgado Eva, Carrión-Barberà Irene, Andreu Jose L, Cobo Tatiana, Horcada Loreto, Bonilla Gema, Lozano-Rivas Nuria, Exposito Lorena, Montilla Carlos, Toyos Francisco J, Ibarguengoitia-Barrena Oihane, Valls Pascual Elia, Nóvoa Medina Javier, Menor-Almagro Raúl, Roman Ivorra Jose Andrés, Muñoz Jiménez Alejandro, Nolla Joan M, Pego-Reigosa Jose Maria
Аннотация
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 χ² (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.