Oral Presentation Abstracts

Introduction: Rheumatic diseases in children have unique patterns of presentation which may be significantly different from their adult counterparts.There has been various studies across the world regarding the presenting features of pediatric rheumatology and children with juvenile idiopathic arthritis.Published data on presenting manifestations of pediatric Rheumatologic diseases is scarce from India.We wanted to study the most common presenting symptom in children presenting to the rheumatology out-patient department and the most common type of JIA in children.Method: In this retrospective study 171 children below the age of 18 years and diagnosed with rheumatic disease was included.The frequency of common rheumatic complaints was retrieved.Other parameters like type of joints involved, systemic manifestations, height and weight parameters was analyzed.Results: A total of 171 children had rheumatic diseases.Out of that 85 (46%) were female.Their average duration of illness as 38 weeks.Most common disease was JIA in 104 (62%) children followed by SLE in 19 (11%) and JDM in 8(4%).Among JIA, OJIA (31 children-30%) was seen in majority of children (figure 1).Commonest complaint was joint pain seen in 130 (77%) children and involvement of large joints (98 children-71%) was more common than small joint involvement.Morning stiffness was seen in 51 (13.9%) children with JIA.HLA B27 was positive in 17 out of 19 ERA patients, ANA was positive in 14 out of 18 screened and Rheumatoid factor was positive in 2 out of 5 screened.One child with OJIA had anterior uveitis.Fever (40 children-44%) and Rash (14 children-34.9%)was the next most common presentation.Proximal muscle weakness was seen in 8 children.Their growth parameters at presentation showed that the height percentile was significantly lower in children with longer duration of chronic illness (r -0.38/p < 0.0001) (figure 2). Conclusion:Apart from joint manifestations, fever, rash, proximal muscle weakness, oral ulcers were other presenting features of Rheumatic diseases in children.There is significant delay in referral to tertiary care entre and thus opportunity for early treatment is lost.ABSTR ACT (UpSpA) is similar but without known precipitating infection, or psoriasis or inflammatory bowel disease.Around 50-70% of these remit spontaneously while the rest become chronic.We have previously described novel gut-microbiota associated with ReA/UpSpA 1 .Now, we attempted to predict the chronicity of ReA using their baseline gut-microbiota.Methods: ReA and UpSpA recruited in the previous study 1 were followed up at 3 years after the initial recruitment.They were classified as having "Chronic ReA" (still on DMARDs or having symptoms of SpA) or in drug-free "Remission".Differentially abundant microbiota between Chronic ReA and Remission groups was analyzed.Further, the groups were randomly divided into training (80% data) and validation (20% data) sets.Clinical features (disease duration, DMARD use, HLA-B27 status) and microbiota of differential abundant families were analyzed by machine learning (Support Vector Machine: SVM and Linear Regression: LR) for models to predict the chronicity of ReA.Results: Out of the initial 55 cohort, 29 patients (median age: 29 years; females: 10) consented for the follow-up.HLA-B27 was positive in 14(48.3%)and 4(13.7%) had axial involvement.At median 3 years follow-up, 14 (48.3%)patients had chronic ReA while the rest were in drug-free remission.Alpha diversity (Observed, Shannon, Simpson, Fisher, InvSimpson) as well as Beta diversity (Bray-Curtis and Unweighted UniFrac) were similar between the two groups.After excluding OTUs whose frequency was less than 5 and present in less than half the samples, the differential abundance is shown at the Phylum (Figure 1A) and Class levels (Figure 1B).Among microbiota whose abundance was at least log-2-fold different between the groups, we identified 29 OTUs (Figure 2A) whose difference was statistically significant (FDR <0.05).Among the top discriminating OTUs, Bacteroides ovatus (log2fd = 3.19), Salmonella sp.HNK130(2.76),Escherichia coli (2.54), Bacteroides fragilis (2.51) that were more abundant in Chronic ReA while Veillonella parvula (-2.74),Prevotella dentalis (-2.76), and Bifidobacterium adolescentis (-2.76) were more abundant in those in remission.In the SVM model, the most accurate model was the one incorporating HLA-B27, the use of methotrexate or sulfasalazine, and the families Enterobacteriaceae and Bifidobacteriaceae.The validation cohort showed an accuracy of 0.83 (95% CI: 0.36-0.99).Figure 2B summarizes that species most important for prediction in the LR model. Conclusion:We have identified microbiota that can predict the chronicity of ReA.The prediction can be improved upon with larger cohorts with prospective validation.