Lupus Science and Medicine (Jun 2022)

Major infections in newly diagnosed systemic lupus erythematosus: an inception cohort study

  • Fangfang Sun,
  • Wanlong Wu,
  • Shuang Ye,
  • Nan Shen,
  • Yi Chen,
  • Shikai Geng,
  • Haiting Wang,
  • Liling Zhao,
  • Danting Zhang,
  • Yuhong Zhou,
  • Liqin Yu

DOI
https://doi.org/10.1136/lupus-2022-000725
Journal volume & issue
Vol. 9, no. 1

Abstract

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Objective To evaluate the risk of major infections and the relationship between major infections and mortality in patients with newly diagnosed SLE.Methods A newly diagnosed (<3 months) hospitalised Systemic Lupus Inception Cohort (hSLIC) in our centre during 1 January 2013 and 1 November 2020 was established. All patients were followed up for at least 1 year or until death. Patient baseline characteristics were collected. Major infection events were recorded during follow-up, which were defined as microbiological/clinical-based diagnosis treated with intravenous antimicrobials. The cohort was further divided into a training set and a testing set. Independent predictors of major infections were identified using multivariable logistic regression analysis. Kaplan-Meier survival analyses were conducted.Results Among the 494 patients enrolled in the hSLIC cohort, there were 69 documented episodes of major infections during the first year of follow-up in 67 (14%) patients. The major infection events predominantly occurred within the first 4 months since enrolment (94%, 65/69) and were associated with all-cause mortality. After adjustments for glucocorticoid and immunosuppressant exposure, a prediction model based on SLE Disease Activity Index >10, peripheral lymphocyte count <0.8×109/L and serum creatinine >104 µmol/L was established to identify patients at low risk (3%–5%) or high risk (37%–39%) of major infections within the first 4 months.Conclusions Newly onset active SLE is susceptible to major infections, which is probably due to underlying profound immune disturbance. Identifying high-risk patients using an appropriate prediction tool might lead to better tailored management and better outcome.