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ObjectiveMany autoantibodies are known to be associated with SLE, although their role in clinical practice is limited because of low sensitivity and weak associations with clinical manifestations. There has been great interest in the discovery of new autoantibodies to use in clinical practice. In this study, we investigated 57 new and known antibodies and their potential for diagnostics or risk stratification.MethodsBetween 2014 and 2017, residual sera of all anti-dsDNA tests in the UMC Utrecht were stored in a biobank. This included sera of patients with SLE, patients with a diagnosis of another immune-mediated inflammatory disease (IMID), patients with low (non-IMID) or medium levels of clinical suspicion of SLE but no IMID diagnosis (Rest), and self-reported healthy blood bank donors. Diagnosis and (presence of) symptoms at each blood draw were retrospectively assessed in the patient records with the Utrecht Patient-Oriented Database using a newly developed text mining algorithm. Sera of patients were analysed for the presence of 57 autoantibodies with a custom-made immunofluorescent microarray. Signal intensity cut-offs for all antigens on the microarray were set to the 95th percentile of the non-IMID control group. Differences in prevalence of autoantibodies between patients with SLE and control groups were assessed.ResultsAutoantibody profiles of 483 patients with SLE were compared with autoantibody profiles of 1397 patients from 4 different control groups. Anti-dsDNA was the most distinguishing feature between patients with SLE and other patients, followed by antibodies against Cytosine-phosphate-Guanine (anti-CpG) DNA motifs (p<0.0001). Antibodies against CMV (cytomegalovirus) and ASCA (anti-Saccharomyces cerevisiae antibodies) were more prevalent in patients with SLE with (a history of) lupus nephritis than patients with SLE without nephritis.ConclusionAntibodies against CpG DNA motifs are prevalent in patients with SLE. Anti-CMV antibodies are associated with lupus nephritis.

Objective Electronic health records (EHR) are increasingly being recognized as a major source of data reusable for medical research and quality monitoring, although patient identification and assessment of symptoms (characterization) remain challenging, especially in complex diseases such as systemic lupus erythematosus (SLE). Current coding systems are unable to assess information recorded in the physician’s free‐text notes. This study shows that text mining can be used as a reliable alternative. Methods In a multidisciplinary research team of data scientists and medical experts, a text mining algorithm on 4607 patient records was developed to assess the diagnosis of 14 different immune‐mediated inflammatory diseases and the presence of 18 different symptoms in the EHR. The text mining algorithm included key words in the EHR, while mining the context for exclusion phrases. The accuracy of the text mining algorithm was assessed by manually checking the EHR of 100 random patients suspected of having SLE for diagnoses and symptoms and comparing the outcome with the outcome of the text mining algorithm. Results After evaluation of 100 patient records, the text mining algorithm had a sensitivity of 96.4% and a specificity of 93.3% in assessing the presence of SLE. The algorithm detected potentially life‐threatening symptoms (nephritis, pleuritis) with good sensitivity (80%‐82%) and high specificity (97%‐97%). Conclusion We present a text mining algorithm that can accurately identify and characterize patients with SLE using routinely collected data from the EHR. Our study shows that using text mining, data from the EHR can be reused in research and quality control.

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Objectives This study aims to gain insight into the care provided to patients with antiphospholipid syndrome (APS) in The Netherlands, and to identify areas for improvement from the perspective of both patients and medical specialists. Methods APS care was evaluated using qualitative and quantitative methods. Perspectives on APS care were explored using semi-structured interviews with medical specialists, patient focus groups and a cross-sectional, online patient survey. In order to assess current practice, medical records were reviewed retrospectively to collect data on clinical and laboratory manifestations and pharmacological treatment in six Dutch hospitals. Results Fourteen medical specialists were interviewed, fourteen patients participated in the focus groups and 79 patients completed the survey. Medical records of 237 patients were reviewed. Medical record review showed that only one-third of patients were diagnosed with APS within three months after entering specialist care. Diagnostic approach and management varied between centres and specialists. Almost 10% of all patients and 7% of triple positive patients with thrombotic APS did not receive any anticoagulant treatment at the time of medical record review. Correspondingly, poor recognition and fragmentation of care were reported as the main problems by medical specialists. Additionally, patients reported the lack of accessible, reliable patient education, psychosocial support and trust in physicians as important points for improvement. Conclusion Delayed diagnosis, variability in management strategies and fragmentation of care were important identified limitations of APS care in this study. A remarkable 10% of patients did not receive any anticoagulant treatment.

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BackgroundSystemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. SLE can lead to premature death as the result of disease activity or because of treatment side effects. This underlines the urgency to identify patients at risk for a complicated disease course, and the need to tailor therapy. Stratification based on immunological manifestations such as autoantibodies, upregulation of type I interferon (IFN) regulated genes (IFN signature) and neutrophil extracellular trap (NET) formation via NETosis can help to improve treatment outcome in SLE.ObjectivesHere we study the association between SLE-related autoantibodies, the IFN signature and NET formation in patients with SLE, which could lead to improved tools for patient stratification and more targeted treatment options.MethodsWe studied the association between the IFN signature and plasma induced NET formation with 57 autoantibodies in 25 patients with SLE. The presence of an IFN signature was determined using the sum of standardized mRNA expression of IFI44L, IFITM1, SERPING1, and LY6E in monocytes from SLE patients. Plasma induced NET formation was studied with quantitative live imaging. The threshold for the presence of an IFN signature or NET formation were both set at 2 SD above the mean of a group of healthy controls. With principal component analysis (PCA) and hierarchical clustering we associated autoantibody concentrations with the IFN signature and NET formation. This study was a separate analysis from larger cohorts, of which results have been previously published.[1,2]ResultsWe observed two distinct clusters with the PCA: one cluster contained mostly patients with an IFN signature, and another cluster contained a mix of patients with (IFN) and without (noIFN) an IFN signature. Patients with (NET) and without (noNET) plasma induced NET formation were equally distributed between the clusters. PC1 explains 22.7% of total variability, and is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. Hierarchical cluster analysis confirmed the two clusters (Figure 1). In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET formation with anti-FcER and anti-PmScl100.ConclusionWe identified a subgroup of patients with an IFN signature who express increased concentrations of antibodies against DNA and RNA-associated proteins. We did not find positive associations between autoantibodies and plasma induced NET formation. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature. As the IFN signature currently is not part of the standard follow-up for patients, partially due to its associated costs, a profile of DNA and RNA-binding autoantibodies might be used for patient stratification, especially related to anti-IFN treatment.References[1]Brunekreef, T., et al. (2021). “Microarray testing in patients with systemic lupus erythematosus identifies a high prevalence of CpG DNA-binding antibodies.” Lupus Sci Med 8(1).[2]van der Linden, M., et al. (2018). “Neutrophil extracellular trap release is associated with antinuclear antibodies in systemic lupus erythematosus and anti-phospholipid syndrome.” Rheumatology (Oxford) 57(7): 1228-1234.Figure 1.A distinct autoantibody pattern is present in a subgroup of patients with an IFN signature. Footnote: Heatmap of Z-scores for 57 autoantibodies in 25 SLE patients. Vertical axis shows clustering of patients based on IFN signature (blue) or NET formation (orange/brown). Horizontal axis represents clustering based on autoantibody profiles.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundThe effectiveness of treat to target (T2T) in RA is widely acccepted, but there is no consensus regarding the best initial treatment in early rheumatoid arthritis1. Therefore, it is important to evaluate the results of such strategies in real life cohorts2,3.ObjectivesCompare the effectiveness of step-up methotrexate (MTX) monotherapy and combination of hydroxychloroquine (HCQ), methotrexate and intramuscular injection of triamcinolone 80–120mg as initial treatment in early RA.MethodsHistorical cohorts of patients treated with MTX monotherapy (disease onset 2006–2011, N=297) and combination therapy (2012–2016, N=156) were compared. In both cohorts a b-DMARD was advised when no remission was reached within 6 months or in case of sustained activity thereafter. Baseline characteristics and disease activity (DAS28) measurements (N=4956, average 4.1/year) in the first 3 years of follow-up were available. The primary outcome measure was the proportion of patients having reached at least one DAS28 <2.6 (remission) during follow-up. Secondary outcomes were sustained remission over 36 months and time to first b-DMARD.ResultsThree patients did not start MTX, and 11 and 2 patients in the step-up and combination cohorts respectively did not have complete follow-up (Table). Within 12 months, more patients on combination treatment reached remission (88,2% vs 72.2%). In the second year these changed to 86.5% and 82.0% respectively. Combination treatment resulted in a higher percentage of DAS measurements below 2.6 over 3 years, reflecting sustained remission (Figure). A b-DMARD was started within 24 months in 20.6% of patients on monotherapy versus 14.1% on combination treatment, with an equal mean time to first b-DMARD of 12 vs 11 months after start of initial treatment.Table 1TableMTX monotherapy (N=297)MTX with HCQ and steroids (N=156) Female, N (%)180 (60.6)101 (64.7)Age (y), mean (SD, range)59.5 (14.3, 18–89)58.8 (12.9, 19–86)Rheumatoid factor (N, %)183 (61.6)112 (71.8)ACPA (N, %)157 (65.3)111 (71.2)Follow-up (months) (range)81 (8–132)42 (11–62)End of follow-up <3 yrs Death4 (1.3%)1 (0.6%) Remission4 (1.3%)1 (0.6%) Did not start MTX/Other5 (1.7%)1 (0.6%)Any remission (DAS28 <2.6) First year72.2%88.2% Second year82.0%86.5% Third year85.9%87.0%Start b-DMARD therapy First year32 (10.8%)17 (10.9%) Second year29 (9.8%)5 (3.2%) Third year8 (2.7%)5/104 (4.8%)ConclusionsCombination treatment results in more remissions in the first year of treatment. In the second and third year the remission percentage on monotherapy comes close to combination treatment, at the cost of a 6% higher proportion of patients stepping up to biologicals. Overall, the combination of MTX with HCQ and triamcinolone results in more sustained remissions.References Stoffer M, Schoels M, Smolen J et al. Evidence for treating rheumatoid arthritis to target: results from a systematic literature search update. Ann Rheum Dis 2016; 75:16–22.de Jong P, Hazes J, Barendregt P et al. Induction therapy with a combination of DMARDs is better than methotrexate mo...