| Autor: |
Loreto Carmona, Carlos Montilla, Ricardo Blanco, Ana Pérez, Íñigo Rúa-Figueroa, Celia Erausquin, Beatriz Tejera Segura, Javier Narváez, Irene Carrión-Barberà, Jorge Juan Fragío Gil, Raúl Menor-Almagro, Antonio Fernández-Nebro, Eva Tomero, Esther Ruiz-Lucea, Julia Martínez-Barrio, Gema Bonilla, Elena Aurrecoechea, María Jesús García-Villanueva, Eva Salgado, Mercedes Freire-González, Jaime Calvo Alen, Tatiana Cobo, María Galindo Izquierdo, Mónica Ibáñez-Barcelo, Loreto Horcada, Lorena Expósito, Joan M Nolla, Alejandro Muñoz-Jiménez, Jose L Andreu, Clara Sanguesa, Nuria Lozano-Rivas, Marta Arévalo, Carlota Iniguez, M Jesus García de Yébenes, Esther Uriarte Itzazelaia, José Carlos Rosas Gómez de Salazar, Silvia Gómez-Sabater, Claudia Moriano Morales, Vicente Torrente Segarra, Javier Nóvoa Medina, Angela Pecondón, Francisco J Toyos, Jose Oller, J M Pego-Reigosa |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Lupus Science and Medicine, Vol 11, Iss 1 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2053-8790 |
| DOI: |
10.1136/lupus-2023-001096 |
| Popis: |
Objective To develop an improved score for prediction of severe infection in patients with systemic lupus erythematosus (SLE), namely, the SLE Severe Infection Score-Revised (SLESIS-R) and to validate it in a large multicentre lupus cohort.Methods We used data from the prospective phase of RELESSER (RELESSER-PROS), the SLE register of the Spanish Society of Rheumatology. A multivariable logistic model was constructed taking into account the variables already forming the SLESIS score, plus all other potential predictors identified in a literature review. Performance was analysed using the C-statistic and the area under the receiver operating characteristic curve (AUROC). Internal validation was carried out using a 100-sample bootstrapping procedure. ORs were transformed into score items, and the AUROC was used to determine performance.Results A total of 1459 patients who had completed 1 year of follow-up were included in the development cohort (mean age, 49±13 years; 90% women). Twenty-five (1.7%) had experienced ≥1 severe infection. According to the adjusted multivariate model, severe infection could be predicted from four variables: age (years) ≥60, previous SLE-related hospitalisation, previous serious infection and glucocorticoid dose. A score was built from the best model, taking values from 0 to 17. The AUROC was 0.861 (0.777–0.946). The cut-off chosen was ≥6, which exhibited an accuracy of 85.9% and a positive likelihood ratio of 5.48.Conclusions SLESIS-R is an accurate and feasible instrument for predicting infections in patients with SLE. SLESIS-R could help to make informed decisions on the use of immunosuppressants and the implementation of preventive measures. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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