Lupus Science and Medicine (Apr 2024)

SLESIS-R: an improved score for prediction of serious infection in patients with systemic lupus erythematosus based on the RELESSER prospective cohort

  • 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

DOI
https://doi.org/10.1136/lupus-2023-001096
Journal volume & issue
Vol. 11, no. 1

Abstract

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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.