Predictive Factors of Hospital-Acquired Bacterial Infections in Patients With Systemic Lupus Erythematosus.

OBJECTIVE: We aimed to identify the predictive factors of hospital-acquired bacterial infections in patients with systemic lupus erythematosus (SLE). METHODS: This chart review study included patients with SLE who were hospitalized between 2009 and 2020 for reasons other than infection. The outcome was defined as any infection confirmed using any bacterial isolation method or diagnosed by treating physicians and required treatment with intravenous antibiotics. For statistical analysis, logistic regression analyses were performed. RESULTS: In total, 1678 patients (87.6% women) were included. The median age was 33 years (interquartile range, 24-47 years). The incidence of hospital-acquired infections was 13.9% (233 infections). Age, Systemic Lupus Erythematosus Disease Activity Index score, Systemic Lupus International Collaborating Clinics damage score, blood urea nitrogen and C-reactive protein levels, dosage of steroid in the previous month, recent use of 1 or more immunosuppressants, admission with a central venous catheter (or dialysis catheter), and use of central venous catheter or bladder catheter in the first 5 days were the predictive factors of nosocomial infections. CONCLUSION: The patients' infection risk profile should be assessed to accurately determine the risk-benefit balance of any therapeutic intervention, minimize exposure to steroids and immunosuppressants, and maintain a low threshold for the early diagnosis of infections. Further studies should assess whether the modification of some identified factors could reduce the incidence of nosocomial infections.

Predictive models of infection in patients with systemic lupus erythematosus: A systematic literature review.

INTRODUCTION: Having reliable predictive models of prognosis/the risk of infection in systemic lupus erythematosus (SLE) patients would allow this problem to be addressed on an individual basis to study and implement possible preventive or therapeutic interventions. OBJECTIVE: To identify and analyze all predictive models of prognosis/the risk of infection in patients with SLE that exist in medical literature. METHODS: A structured search in PubMed, Embase, and LILACS databases was carried out until May 9, 2020. In addition, a search for abstracts in the American Congress of Rheumatology (ACR) and European League Against Rheumatism (EULAR) annual meetings' archives published over the past eight years was also conducted. Studies on developing, validating or updating predictive prognostic models carried out in patients with SLE, in which the outcome to be predicted is some type of infection, that were generated in any clinical context and with any time horizon were included. There were no restrictions on language, date, or status of the publication. To carry out the systematic review, the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline recommendations were followed. The PROBAST tool (A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies) was used to assess the risk of bias and the applicability of each model. RESULTS: We identified four models of infection prognosis in patients with SLE. Mostly, there were very few events per candidate predictor. In addition, to construct the models, an initial selection was made based on univariate analyses with no contraction of the estimated coefficients being carried out. This suggests that the proposed models have a high probability of overfitting and being optimistic. CONCLUSIONS: To date, very few prognostic models have been published on the infection of SLE patients. These models are very heterogeneous and are rated as having a high risk of bias and methodological weaknesses. Despite the widespread recognition of the frequency and severity of infections in SLE patients, there is no reliable predictive prognostic model that facilitates the study and implementation of personalized preventive or therapeutic measures.Protocol registration number: PROSPERO CRD42020171638.