FRI0469 TO DISTINGUISH BETWEEN DISEASE FLARE AND ACTIVE INFECTION IN PEDIATRIC SYSTEMIC LUPUS ERYTHEMATOSUS (SLE)
Authors: K. L. Luo, J. H. Lee, B. L. Chiang
Journal: Annals of the Rheumatic Diseases (2020)

Background: SLE is the autoimmune disease involving multiple systems. Infections might mimic SLE flare, leading to confusion over the diagnosis and appropriate treatment. To distinguishing acute infection from active flare always remains a clinical challenge. Objectives: We aim to explore the potential parameters in identifying active infection and disease activity in pediatric SLE. Methods: We reviewed the medical charts of the pediatric SLE patient in National Taiwan University Hospital from August 2015 to September 2019, and 50 SLE patients presented 185 episodes of suspicious activity or infection and received CRP, ESR, and Procalcitonin measurement were included. Time matched other laboratory parameters and clinical assessments were also collected. Episodes were divided into 4 groups: infected-active, infected-inactive, noninfected-active, and noninfected-inactive. Association of parameters with outcomes were predicted by generalized estimating equation. The receiver operating curve and the area under the curve were used to evaluate the diagnostic performance. We also used multinomial logistic regression model for nominal outcome, by setting noninfected-inactive group as the reference category. Results: There were 7 males (14%) and 43 females (86%), with the mean ages 13.9 ± 4.4 years old. Most of the patients had renal (72%) or mucocutaneous (72%) involvement. The most common infection site was respiratory system (56%). Multivariate GEE analysis showed Damage index(DI), SLEDAI-2k, neutrophil-to-lymphocyte ratio (NLR), hemoglobin, platelet, RDW-to-platelet ratio (RPR), and C3 are independent parameters for predicting SLE activity flare. Combination of these seven parameters resulted in a model with calculated AUC of 0.8964 and with sensitivity of 82.2 % and specificity of 90.9%. Multivariate GEE analysis showed DI, fever, CRP, Procalcitonin, lymphocyte percentage, NLR, hemoglobin, and renal score in SLEDAI-2k are independent parameters for predicting acute infection. These eight parameters resulted in a model with calculated AUC of 0.7886 and with sensitivity of 63.5% and specificity of 89.2%. We select a total of 10 variables (DI, SLEDAI-2k, Fever, Procalcitonin, lymphocyte percentage, NLR, hemoglobin, platelet, RPR, C3) to establish multinomial logistic regression, then predict four groups with accuracy of 70.13% for infected-active, 10% for infected-inactive, 59.57% for noninfected-active, and 84.62% for noninfected-inactive. Conclusion: The proposed predictive calculator could be a useful tool for differentiation between activity flares and acute infections in pediatric SLE. Obtaining and combination of several parameters is effective and helpful to make appropriate judgement and treatment decisions for SLE patients. References: [1]Ceccarelli, F., et al., Assessment of disease activity in Systemic Lupus Erythematosus: Lights and shadows. Autoimmun Rev, 2015. 14 (7): p. 601-8. [2]Jung, J.Y. and C.H. Suh, Infection in systemic lupus erythematosus, similarities, and differences with lupus flare. Korean J Intern Med, 2017. 32 (3): p. 429-438. [3]Gensous, N., et al., Predictive biological markers of systemic lupus erythematosus flares: a systematic literature review. Arthritis Res Ther, 2017. 19 (1): p. 238. [4]Beca, S., et al., Development and validation of a risk calculator to differentiate flares from infections in systemic lupus erythematosus patients with fever. Autoimmun Rev, 2015. 14 (7): p. 586-93. [5]Sciascia, S., et al., Systemic lupus erythematosus and infections: clinical importance of conventional and upcoming biomarkers. Autoimmun Rev, 2012. 12 (2): p. 157-63. [6]Ospina, F.E., et al., Distinguishing infections vs flares in patients with systemic lupus erythematosus. Rheumatology (Oxford), 2017. 56 (suppl_1): p. i46-i54. [7]Ballinger, G.A., Using Generalized Estimating Equations for Longitudinal Data Analysis. Organizational Research Methods, 2004. 7 (2): p. 127-150. Disclosure of Interests: None declared