Background Gene expression analysis is a crucial tool for uncovering the biological mechanisms that underlie differences between patient subgroups, offering insights that can inform clinical decisions. However, despite its potential, gene expression analysis remains challenging for clinicians due to the specialised skills required to access, integrate, and analyse large datasets. Existing tools primarily focus on RNA-Seq data analysis, providing user-friendly interfaces but often falling short in several critical areas: they typically do not integrate clinical data, lack support for patient-specific analyses, and offer limited flexibility in exploring relationships between gene expression and clinical outcomes in disease cohorts. Users, including clinicians with a general knowledge of transcriptomics, however, who may have limited programming experience, are increasingly seeking tools that go beyond traditional analysis. To overcome these issues, computational tools must incorporate advanced techniques, such as machine learning, to better understand how gene expression correlates with patient symptoms of interest. Results Our RNAcare platform, addresses these limitations by offering an interactive and reproducible solution specifically designed for analysing transcriptomic data from patient samples in a clinical context. This enables researchers to directly integrate gene expression data with clinical features, perform exploratory data analysis, and identify patterns among patients with similar diseases. By enabling users to integrate transcriptomic and clinical data, and customise the target label, the platform facilitates the analysis of the relationships between gene expression and clinical symptoms like pain and fatigue. This allows users to generate hypotheses and illustrative visualisations/reports to support their research. As proof of concept, we use RNAcare to link inflammation-related genes to pain and fatigue in rheumatoid arthritis (RA) and detect signatures in the drug response group, confirming previous findings. Conclusion We present a novel computational platform allowing the interpretation of clinical and transcriptomics data in real-time. The platform can be used for data generated by the user, such as the patient data presented here or using published datasets. The platform is available at https://rna-care.mvls.gla.ac.uk/ , and its source code is https://github.com/sii-scRNA-Seq/RNAcare/ .

Rheumatoid arthritis (RA) is an autoimmune disease characterized by the presence of autoantibodies against modified proteins, known as anti-modified protein autoantibodies (AMPAs). While the relationship between different AMPA isotypes and various risk factors remains poorly understood, investigating this association is important for a deeper understanding of RA pathophysiology. Smoking, has its primary effects in the lungs, and it remains unclear whether smoking is preferentially linked to specific AMPA isotypes, such as IgA, which could suggest a mucosal origin. Therefore, we set out to investigate the association between smoking, genetic risk factors for RA, and the presence of specific AMPA isotypes, particular IgA. In 618 RA patients, anti-citrullinated protein antibodies (ACPA-) and anti-acetylated protein antibodies (AAPA-) IgA, -IgG and -IgM and RF-IgA and -IgM were measured by ELISA. Associations with genetic risk factors, smoking and autoantibodies were assessed with logistic regression analysis. For replication, a comprehensive meta-analysis incorporating 3309 RA patients was performed. Smoking was primarily associated with IgA AMPA, with associations that prevailed after correcting for the concurrent presence of AMPA IgG (ACPA-IgA OR 1.89 [1.14–3.12], AAPA-IgA 2.30 [1.35–3.94]). To further substantiate these results, we performed a meta-analysis of 3309 RA patients and observed that smoking was again predominantly associated with the combined presence of ACPA-IgA in addition to ACPA-IgG (OR 2.05 [1.69–2.49], p < 0.001) versus the single presence of ACPA-IgG (OR 1.18 [0.97–1.44], p = 0.11). A gene-environment interaction between the most important genetic risk factor for RA (the HLA shared epitope alleles) and smoking was only seen in patients that were both ACPA-IgG and ACPA-IgA positive, but not in patients who were only positive for ACPA-IgG. These data provide a pivotal refinement of existing knowledge regarding risk factor associations for RA and lend novel support to the hypothesis that smoking may exert its effect on RA by the induction of local (auto)immune responses at mucosal sites.

Rheumatoid arthritis (RA) develops after progressing through sequential 'pre-RA' phases. The mechanisms driving progression from one phase to the next remain poorly understood. This study examined the longitudinal rates of community and hospital infections in patients during sequential stages of pre-RA and early arthritis.Methods The Scottish Early RA inception cohort recruited patients with newly diagnosed RA. Incidences of infection were determined from community antibiotic prescriptions and serious infections were determined by hospital discharge coding. Dates of diagnosis and symptom onset allowed identification of asymptomatic/symptomatic pre-RA and early arthritis eras to analyse infection rates over time compared with age- and sex-matched controls.Results The incidence rate ratio (IRR) seen in the period 0-6 months prior to symptom onset was 1.28 (95% CI 1.15 to 1.42). In 'symptomatic pre-RA', the IRR was 1.33 (95% CI 1.18 to 1.49) which persisted into 'early arthritis'. The rate of hospital admissions was numerically greater in 'pre-RA' and significantly greater in 'early arthritis' (IRR 1.82, 95% CI 1.32 to 2.46).Conclusion Antibiotic risk is increased in patients with 'pre-RA' at least 6 months before symptoms develop, and this persists throughout the symptomatic pre-RA phase. Infections may be important in the mechanisms that drive progression to RA or be a manifestation of immune dysfunction (or both). These observations could inform safety and efficacy considerations for interventions in pre-RA to prevent progression. Patients with 'pre-RA' with recurrent antibiotic use may also be an identifiable 'high risk' group that could enrich the study population for intervention studies in pre-RA.

Background Retroperitoneal fibrosis (RPF) is a rare condition characterised by the inflammation and fibrosis of tissues in the retroperitoneum. Often presenting non-specifically with a variable picture of abdominal pain, constipation, and systemic symptoms of malaise, fever, and weight loss. RPF may also present with severe obstructive uropathy. Initial response to steroids is usually good, but a significant proportion of patients relapse. Evidence for second line treatment is scarce, and a variety of immunosuppressive therapies, such as rituximab, have been used. Surgical intervention and interventional radiology may be useful in treating acute ureteric obstruction and preserving renal function. Objectives As an orphan disease without, traditionally, a clear home speciality, retroperitoneal disease is likely underdiagnosed and undertreated especially in those who do not have ureteric obstruction. We characterised patients with retroperitoneal fibrosis across West and South-East Scotland to better understand these patients and their outcomes. Methods Patients were previously identified in East Scotland by contacting rheumatologists and by searching the urological stent registry from 2019 to 2022. The method was replicated in the West of Scotland though was less successful as the urological stent register did not record diagnoses, and clinicians did not have an accessible database for this condition. A further avenue of patient identification was then pursued. All available radiological reports and their attached clinical data on the request from 2006 to September 2022 were searched for the term “RPF” and “retroperitoneal fibrosis”. Patients were included if they were given a clinical diagnosis of retroperitoneal fibrosis. Patients were excluded if RPF was concurrent with metastatic malignancy, associated with infection or other secondary cause. Patient data was acquired from their electronic records. Results 78 patients were identified: 56 in the West of Scotland and 22 in the South-East. Patient demographics are shown in Table 1. The diagnosis was most often made by CT scan. The estimated annual incidence in the last 10 years 2.2 is per million population in the West and 1.6 per million population in the East, for catchment populations of 1.2 million and 0.9 million, respectively. Treatment was heterogenous in both groups. Renal outcomes for these patients were interrogated. Patients were classified as having lost renal function if there was either biochemical or radiological evidence. Conclusion The literature suggests renal impairment is variable in retroperitoneal fibrosis with only 32% of patients with abnormal creatinine reported in one study which is much lower than our results. The current literature is sparce for alternative therapies. More work is required in determining efficacy for more recent therapies such as rituximab to further the treatment of this orphan disease and improve patient outcomes. References [1]Vaglio A, Maritati F. Idiopathic Retroperitoneal Fibrosis. J Am Soc Nephrol. 2016;27:1880-9.[2]Kermani TA, Crowson CS, Achenbach SJ, Luthra HS. Idiopathic retroperitoneal fibrosis: a retrospective review of clinical presentation, treatment, and outcomes. Mayo Clin Proc. 2011;86:297-303. Acknowledgements Acknowledgements to the Rheumatology and Urology team at NHS Lothian and NHS GGC for patient identification. Acknowledgements to Greater Glasgow and Clyde Safe Haven Data Team for their assistance with accessing databases for patient identification. Disclosure of Interests None Declared.Table 1Summary of findings in the two cohorts across Scotland compared with literature.South-East ScotlandWest ScotlandLiteratureNumber2256Age at diagnosis60y (range 43-81y)61.5y (range 45-83y)54-64y1Female: male ratio1:2.71:1.91:1.2-3.41Lost renal function73% (16)75% (42)eGFR <60ml/min/1.73m273% (16)61% (34)32%2eGFR <30 ml/min/1.73m227% (6)18% (10)

Background Delineating systemic immune cell signatures in rheumatoid arthritis (RA) has the potential for understanding pathogenesis and stratifying therapeutics accordingly. Objectives To evaluate the phosphoprotein signatures in circulating lymphocytes of early RA patients compared with healthy controls across disease trajectory over one year. Methods We used phospho-flow cytometry to measure the phosphorylation state of phosphoproteins in basal (unstimulated), and ex vivo stimulated circulating lymphocytes from 55 early RA patients before and after 6- and 12-month treatment with methotrexate (MTX) and compared these to 37 age- and gender-matched healthy controls. Flow cytometry datasets were analysed using FlowSOM, an unbiased clustering algorithm that partitions cell populations based on marker expression patterns. Results Multiple differential phosphorylation signatures are expressed across CD4+ and CD8+ T-cells and CD19+ B-cells that differentiate baseline RA from healthy controls; the signatures normalised towards a healthy state following treatment with MTX. Stimulation of cells from RA patients at baseline is refractory to further stimulus-induced phosphorylation, which was recovered following MTX treatment (p<0.01). Conclusion This comprehensive and unbiased analysis identified discrete clusters of cells exhibiting unique phosphoprotein signatures associated with early RA that differentiates the systemic immune response from healthy controls. Methotrexate treatment recovers this phosphoprotein signature towards a healthy state. References [1]Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet 2016; 388:2023-38. [2]McInnes IB, Schett G. The pathogenesis of rheumatoid arthritis. N Engl J Med 2011;365:2205-19. [3]McInnes IB, Buckley CD, Isaacs JD. Cytokines in rheumatoid arthritis - shaping the immunological landscape. Nat Rev Rheumatol 2016;12:63-8. [4]Van Gassen S, Callebaut B, Van Helden MJ, et al. FlowSOM: Using self-organising maps for visualisation and interpretation of cytometry data. Cytometry 2015;87:636-45. Acknowledgements: NIL. Disclosure of Interests Mukanthu Nyirenda: None declared, Moeed Akbar: None declared, Ashley Gilmour: None declared, Carol Wallace: None declared, Caron Paterson: None declared, Duncan Porter Consultant of: Abbvie and Eli Lilly, Grant/research support from: Abbvie and Eli Lilly, David Reid: None declared, Janet Liversidge: None declared, Iain McInnes Consultant of: Abbvie, Amgen, Eli Lilly, Novartis, Janssen, UCB, Bristol Myers Squibb, Cabaletta, Compugen, MoonLake, Pfizer, and Astra Zeneca., Grant/research support from: UCB, Bristol Myers Squibb, Novartis, Astra Zeneca, and Eli Lilly., Carl Goodyear: None declared.

Objectives Macrophage subsets, activated by T cells, are increasingly recognised to play a central role in rheumatoid arthritis (RA) pathogenesis. Janus kinase (JAK) inhibitors have proven beneficial clinical effects in RA. In this study, we investigated the effect of JAK inhibitors on the generation of cytokine-activated T (Tck) cells and the production of cytokines and chemokines induced by Tck cell/macrophage interactions. Methods CD14 + monocytes and CD4 + T cells were purified from peripheral blood mononuclear cells from buffy coats of healthy donors. As representative JAK inhibitors, tofacitinib or ruxolitinib were added during Tck cell differentiation. Previously validated protocols were used to generate macrophages and Tck cells from monocytes and CD4 + T cells, respectively. Cytokine and chemokine including TNF, IL-6, IL-15, IL-RA, IL-10, MIP1α, MIP1β and IP10 were measured by ELISA. Results JAK inhibitors prevented cytokine-induced maturation of Tck cells and decreased the production of proinflammatory cytokines TNF, IL-6, IL-15, IL-1RA and the chemokines IL-10, MIP1α, MIP1β, IP10 by Tck cell-activated macrophages in vitro (p<0.05). Conclusions Our findings show that JAK inhibition disrupts T cell-induced macrophage activation and reduces downstream proinflammatory cytokine and chemokine responses, suggesting that suppressing the T cell-macrophage interaction contributes to the therapeutic effect of JAK inhibitors.

OBJECTIVE:To investigate the association of severe coronavirus disease 2019 (COVID-19) in patients with inflammatory rheumatic diseases (IRDs) treated with immunosuppressive drugs. METHOD:A list of 4633 patients on targeted - biological or targeted synthetic - DMARDs in March 2020 was linked to a case-control study that includes all cases of COVID-19 in Scotland. RESULTS:By 22 November 2021, 433 of the 4633 patients treated with targeted DMARDS had been diagnosed with COVID-19, of whom 58 had been hospitalized. With all those in the population not on DMARDs as the reference category, the rate ratio for hospitalized COVID-19 associated with DMARD treatment was 2.14 [95% confidence interval (CI) 2.02-2.26] in those on conventional synthetic (cs) DMARDs, 2.01 (95% CI 1.38-2.91) in those on tumour necrosis factor (TNF) inhibitors as the only targeted agent, and 3.83 (95% CI 2.65-5.56) in those on other targeted DMARDs. Among those on csDMARDs, rate ratios for hospitalized COVID-19 were lowest at 1.66 (95% CI 1.51-1.82) in those on methotrexate and highest at 5.4 (95% CI 4.4-6.7) in those on glucocorticoids at an average dose > 10 mg/day prednisolone equivalent. CONCLUSION:The risk of hospitalized COVID-19 is elevated in IRD patients treated with immunosuppressive drugs compared with the general population. Of these drugs, methotrexate, hydroxychloroquine, and TNF inhibitors carry the lowest risk. The highest risk is associated with prednisolone. A larger study is needed to estimate reliably the risks associated with each class of targeted DMARD.

Background:Large numbers of patients with rheumatoid arthritis (RA) receive regular opioids despite significant toxicity and a lack of evidence supporting their use in non-cancer pain. In order to address this situation, we need to understand when opioids are started in early RA where this has not been studied.Objectives:To examine the temporal trend of opioid prescriptions before and after RA symptom onset and to compare this with DMARD and NSAID prescriptions.Methods:RA participants (cases) were recruited as part of the Scottish Early Rheumatoid Arthritis (SERA) inception cohort1. Controls without RA (five per case), matched for sex, age and post code over the same time period, were obtained through routine data linkage. Prescription data between Jan 2009 to Nov 2019 of cases and matched controls were compared using date of RA symptom onset as reference point. The Prescriptions Per Participant (PPP) for each three-month block was estimated by dividing the number of prescribed drugs in the selected drug classes (assigned using the British National Formulary) in that time block by the number of participants in each group. The differences between mean PPP of the RA cases and controls in each time block were tested by t-test for independent groups and subsequent adjustment for multiple testing.Results:1,720,335 prescriptions were available for analysis with 421,961 items for 950 RA cases and 1,299,374 items for 4,558 matched controls. As expected, DMARD prescriptions in the SERA cases increased after the symptom onset period and were then sustained (Figure 1: top left panel). NSAID prescriptions in RA cases peaked during the 3 months after symptom onset and then reduced progressively (top right panel). Opioid analgesic prescriptions for the RA cases increased two-fold during the reference period and then reduced 6-9 months post-symptom onset. However, unlike NSAIDs, after this there was no further significant reduction in opioid prescriptions in the RA cases, which remained stable and significantly higher than in the controls for the remaining study period. The non-opioid analgesic mean PPP increased sharply at the time of symptom onset, with a steady gradual upward trend over time (lower right panel).Conclusion:Opioid prescriptions increase significantly at the time of RA symptoms onset. Despite rapid introduction of DMARDs and resultant reductions in NSAIDs, analgesic use remains significantly higher than in controls. Further research is required to identify the factors associated with persistent opioid use in early RA with interventions aimed at the first 6 months.References:[1]Dale et al. BMC Musculoskelet Disord. 2016;17:461.Acknowledgements:The work was supported by Health Data Research UK which receives its funding from HDR UK Ltd funded by the UKRI MRC, EPSRC, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientists Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation (BHF) and the Wellcome Trust.The SERA study was jointly funded by the Chief Scientists Office Scotland and Pfizer Ltd.Disclosure of Interests:None declared

OBJECTIVE:Omission of foot joints from composite global disease activity indices may lead to underestimation of foot and overall disease in rheumatoid arthritis (RA) and under-treatment. The aim of this study was to evaluate the measurement properties of the Rheumatoid Arthritis Foot Disease Activity Index-5 (RADAI-F5), a newly developed patient-reported outcome measure for capturing foot disease activity in people with RA. METHODS:Participants with RA self-completed the RADAI-F5, modified Rheumatoid Arthritis Disease Activity Index (mRADAI-5), Foot Function Index (FFI), and Foot Impact Scale (FIS) impairment/footwear and activity/participation subscales. The 28-joint Disease Activity Score using the erythrocyte sedimentation rate (DAS28-ESR) was also recorded. Subgroups completed the RADAI-F5 at 1 week and 6 months. Psychometric properties, including construct, content and longitudinal validity, internal consistency, 1-week reproducibility, and responsiveness over 6 months were evaluated. RESULTS:Of 142 respondents, 103 were female, with a mean ± SD age of 55 ± 12.5 years and median RA disease duration of 10 (interquartile range 3.6-20.8) months. Theoretically consistent associations confirming construct validity were observed with mRADAI-5 (0.789 [95% confidence interval (95% CI) 0.73, 0.85]), FFI (0.713 [95% CI 0.62, 0.79]), FIS impairment/footwear (0.695 [95% CI 0.66, 0.82], P < 0.001), FIS activity/participation (0.478 [95% CI 0.37, 0.63], P < 0.001), and the DAS28-ESR (0.379 [95% CI 0.26, 0.57], P < 0.001). The RADAI-F5 demonstrated high internal consistency (Cronbach's α = 0.90) and good reproducibility (intraclass correlation coefficient = 0.868 [95% CI 0.80, 0.91], P < 0.001, smallest detectable change = 2.69). Content validity was confirmed, with 82% rating the instrument relevant and easy to understand. CONCLUSION:The RADAI-F5 is a valid, reliable, responsive, clinically feasible patient-reported outcome measure for measuring foot disease activity in RA.

Patients who achieve remission promptly could have a specific genetic risk profile that supports regaining immune tolerance. The identification of these genes could provide novel drug targets.To test the association between RA genetic risk variants with achieving remission at 6 months.We computed genetic risk scores (GRS) comprising of the RA susceptibility variants1 and HLA-SE status separately in 4425 patients across eight datasets from inception cohorts. Remission was defined as DAS28CRP<2.6 at 6 months. Missing DAS28CRP values in patients were imputed using predictive mean matching by MICE. We first tested whether baseline DAS28CRP changed with increasing GRS using linear regression. Next, we calculated odds ratios for GRS and HLA-SE on remission using logistic regression. Heterogeneity of the outcome between datasets was mitigated by running inverse variance meta-analysis.Evaluation of the complete dataset, baseline clinical variables did not differ between patients achieving remission and those who did not (Table 1). Distribution of GRS was consistent between datasets. Neither GRS nor HLA-SE was associated with baseline DAS2DAS (OR1.01; 95% CI 0.99-1.04). A fixed effect meta-analysis (Figure 1.) showed no significant effect of the GRS (OR 0.99; 95% CI 0.94-1.03) or HLA-SE (OR 0.8CRP87; 95% CI 0.75-1.01) on remission at 6 months.Table 1.Summary of the data separated by disease activity after 6 months.allRemission at 6 monthsNo remission at 6 monthsN4425*15582430Age, mean (sd)55.38 (13.87)5517 (14.09)55.62 (13.59)Female %68.98%65.43%70.73%ACPA+ %61.94%63.53%61.67%Baseline DAS28, mean (sd)4.76 (1.22)4.47 (1.23)5.1 (1.15)*not all patients had 6 months dataIn these combined cohorts, RA genetics risk variants are not associated with early disease remission. At baseline there was no difference in genetic risk between patients achieving remission or not. Studies encompassing other genetic variants are needed to elucidate the genetics of RA remission.[1]Knevel R et al. Sci Transl Med. 2020;12(545):eaay1548.This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777357, RTCure.This project has received funding from Pfizer Inc.Samantha Jurado Zapata: None declared, Marc Maurits: None declared, Yann Abraham Employee of: Pfizer, Erik van den Akker: None declared, Anne Barton: None declared, Philip Brown: None declared, Andrew Cope: None declared, Isidoro González-Álvaro: None declared, Carl Goodyear: None declared, Annette van der Helm - van Mil: None declared, Xinli Hu Employee of: Pfizer, Thomas Huizinga: None declared, Martina Johannesson: None declared, Lars Klareskog: None declared, Dennis Lendrem: None declared, Iain McInnes: None declared, Fraser Morton: None declared, Caron Paterson: None declared, Duncan Porter: None declared, Arthur Pratt: None declared, Luis Rodriguez Rodriguez: None declared, Daniela Sieghart: None declared, Paul Studenic: None declared, Suzanne Verstappen: None declared, Leonid Padyukov: None declared, Aaron Winkler Employee of: Pfizer, John D Isaacs: None declared, Rachel Knevel Grant/research support from: Pfizer

BACKGROUND:A range of anti-modified protein antibodies (AMPAs) are associated with rheumatoid arthritis. We aimed to assess the relationship between AMPA profiles and radiographic progression in patients with new-onset rheumatoid arthritis. METHODS:In this cohort study, we obtained samples and data from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort and biobank, which recruited patients with new-onset rheumatoid arthritis or undifferentiated arthritis who had at least one swollen joint from 20 hospitals across Scotland. AMPAs in plasma samples were measured by ELISAs at baseline. Paired radiographs of the hands and feet were taken at baseline and at 1 year and were scored with the Sharp-van der Heijde (SvH) method. We calculated differences in radiographic progression using estimated marginal mean changes between baseline and 1 year, with the baseline values of radiographic variables, rheumatoid factor, sex, age at recruitment, symptom duration, and Disease Activity Score 28 with C-reactive protein included as covariates. FINDINGS:Between March 1, 2011, and April, 30, 2015, 1073 patients were recruited to the SERA study. 362 patients with rheumatoid arthritis were included in our study and had their AMPA profiles determined. Patients were grouped into four main autoantibody profiles by reactivities to post-translational modifications: single positivity for anti-citrullinated peptide antibodies (ACPAs; 73 [20%]); double positivity for ACPAs and anti-acetylated peptide antibodies (AAPAs; 45 [12%]); triple positivity for ACPAs, AAPAs, and anti-carbamylated peptide antibodies (151 [42%]); and AMPA negativity (74 [20%]). 19 (5%) patients were in one of the minor autoantibody groups. Of the 233 patients with both antibody data and radiographs of sufficient quality, triple-positive patients had more radiographic progression between baseline and 12 months (estimated mean change in total SvH score 1·8, 95% CI 0·9-2·6, SE 0·4) than did single-positive patients (0·5, 0·1-1·0, 0·2; estimated mean difference in the total change in SvH score 1·2, 95% CI 0·1-2·4, SE 0·5). There was no difference in radiographic progression between single positive patients and AMPA negative patients (estimated mean change in total SvH score 0·7, 95% CI 0·1-1·4, SE 0·3; estimated mean difference in the total change in SvH score -0·2, 95% CI -1·1 to 0·7, SE 0·4). INTERPRETATION:This study suggests that the optimal prediction of future rates of radiographic progression in patients with rheumatoid arthritis will require an assessment of autoantibodies against multiple post-translationally modified proteins or peptides. FUNDING:The EU FP7 HEALTH programme, the Scottish Translational Medicine Research Collaboration, and the Chief Scientist Office Scotland.

Introduction and Objectives Severe coronavirus 19 disease (COVID 19) has rapidly emerged as a global health threat and, despite considerable advances, outcomes remain poor in many patients. Published data infers considerable heterogeneity, with 80% suffering minimal symptoms but a minority developing life-threatening disease. COVID 19 trials to-date have been necessarily broad but the emergence of established therapies (e.g. Dexamethasone) and distinct phenotypes (e.g. immune activated, prothrombotic) suggests that early stratification to licensed or trial agents might result in improved outcomes. The ASTERIX study aims to define disease endotypes, based on baseline biological signatures associated with COVID-19 pneumonia, development of respiratory failure and death, which could be targeted in future trials. Methods >6,000 samples of blood, urine and respiratory secretions were collected and banked during the first wave of the COVID 19 pandemic in Glasgow. The cohort is organised into Tiers 0, 1 & 2 with each tier having an increasing number of samples available for downstream translational research. All tiers have the same associated comprehensive clinical data including comorbidity, ethnicity, blood results, imaging, prescription data and outcomes, including critical care support and survival. Results Tier 0 contains 1,512 cases, Tier 1 (defined by having at least one surplus sample banked for downstream assays) contains ~1000 cases. Tier 2 (defined as having matched samples of serum, plasma and a buffy coat) contains 421 cases. Sample collation and data analysis is ongoing but preliminary review indicates a mortality rate of 29%, which is consistent with that reported in UK-wide COVID 19 series. The project team have made extensive links with collaborators and a scientific review board has been convened. The following projects are at various stages of approval and delivery: (1) Host Epigenomics (2) Host Proteomics (3) Host Metabolomics (4) miRNA Outcome Signatures (5) Host Respiratory Microbiome (6) COVID 19 Coagulopathy. Conclusions Data and banked samples will be used to develop endotypes (biological signatures derived from statistical models) associated with progression to key clinical outcomes. This information will be used to identify high-risk cohorts that could be targeted in future studies testing suitable interventions, as directed by the content of each signature.

Background: The American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) individually and collaboratively have produced/recommended diagnostic classification, response and functional status criteria for a range of different rheumatic diseases. While there are a number of different resources available for performing these calculations individually, currently there are no tools available that we are aware of to easily calculate these values for whole patient cohorts. Objectives: To develop a new software tool, which will enable both data analysts and also researchers and clinicians without programming skills to calculate ACR/EULAR related measures for a number of different rheumatic diseases. Methods: Criteria that had been developed by ACR and/or EULAR that had been approved for the diagnostic classification, measurement of treatment response and functional status in patients with rheumatoid arthritis were identified. Methods were created using the R programming language to allow the calculation of these criteria, which were incorporated into an R package. Additionally, an R/Shiny web application was developed to enable the calculations to be performed via a web browser using data presented as CSV or Microsoft Excel files. Results: acreular is a freely available, open source R package (downloadable from https://github.com/fragla/acreular ) that facilitates the calculation of ACR/EULAR related RA measures for whole patient cohorts. Measures, such as the ACR/EULAR (2010) RA classification criteria, can be determined using precalculated values for each component (small/large joint counts, duration in days, normal/abnormal acute-phase reactants, negative/low/high serology classification) or by providing “raw” data (small/large joint counts, onset/assessment dates, ESR/CRP and CCP/RF laboratory values). Other measures, including EULAR response and ACR20/50/70 response, can also be calculated by providing the required information. The accompanying web application is included as part of the R package but is also externally hosted at https://fragla.shinyapps.io/shiny-acreular . This enables researchers and clinicians without any programming skills to easily calculate these measures by uploading either a Microsoft Excel or CSV file containing their data. Furthermore, the web application allows the incorporation of additional study covariates, enabling the automatic calculation of multigroup comparative statistics and the visualisation of the data through a number of different plots, both of which can be downloaded. Figure 1. The Data tab following the upload of data. Criteria are calculated by the selecting the appropriate checkbox. Figure 2. A density plot of DAS28 scores grouped by ACR/EULAR 2010 RA classification. Statistical analysis has been performed and shows a significant difference in DAS28 score between the two groups. Conclusion: The acreular R package facilitates the easy calculation of ACR/EULAR RA related disease measures for whole patient cohorts. Calculations can be performed either from within R or by using the accompanying web application, which also enables the graphical visualisation of data and the calculation of comparative statistics. We plan to further develop the package by adding additional RA related criteria and by adding ACR/EULAR related measures for other rheumatic disorders. Disclosure of Interests: Fraser Morton: None declared, Jagtar Nijjar Shareholder of: GlaxoSmithKline plc, Consultant of: Janssen Pharmaceuticals UK, Employee of: GlaxoSmithKline plc, Paid instructor for: Janssen Pharmaceuticals UK, Speakers bureau: Janssen Pharmaceuticals UK, AbbVie, Carl Goodyear: None declared, Duncan Porter: None declared

The last decade has seen the development or renewal of classification criteria in sundry rheumatic diseases, including systemic lupus erythematosus (SLE),1 rheumatoid arthritis2 and axial spondyloarthritis (axSpA).3 These criteria seek the laudable aim of standardising the populations included in clinical trials and observational cohorts for research purposes. But to what extent have the benefits of classification criteria been realised? Have there been unintended consequences as their profile has grown? And could we better use criteria to achieve the desired end of facilitating the implementation and interpretation of research findings to enable their translation into clinical practice?We are all familiar with the refrain of key opinion leaders when they present their update on the management of a disease—‘These are classification criteria, not diagnostic criteria …’ But how often have we heard the same speaker move smoothly on to state, ‘… but I find them helpful in clinical practice too’? Indeed, the abstract of the original paper describing the validation and final selection of the classification criteria for axSpA concluded with the statement that ‘The new Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axial spondyloarthritis (SpA) … may help rheumatologists in clinical practice in diagnosing axial SpA in those with chronic back pain’.3However, the dangers of applying classification criteria to clinical practice for the purpose of diagnosis are easily demonstrated. The criteria are invariably developed and validated in specialist centres where there is a high prior (pretest) probability of the disease, and they are evaluated based on a clinical diagnosis of the disease in question made by experts in that condition, after alternative causes or explanations for the patient’s symptoms have been excluded. When the criteria are applied to a different population—for instance, in primary care—where there is a low prior (pretest) probability of …