400 research outputs found

    Serious infection across biologic treated patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis

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    ObjectivesTo compare the incidence of serious infection (SI) across biologic drugs used to treat rheumatoid arthritis using data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA). MethodsThe BSRBR-RA is a prospective observational cohort study. This analysis included patients with RA starting a new biologic. The primary outcome was SI defined as an infectious event requiring admission to hospital, intravenous antibiotics or resulting in death. Event rates were calculated and compared across biologics using Cox proportional hazards with adjustment for potential confounders. Secondary outcomes were the rate of infection by organ class and 30-day mortality following infection.ResultsThis analysis included 19,282 patients with 46,771 years of follow-up. The incidence of SI was 5.51 cases per 100 patient years for the entire cohort (95% CI 5.29, 5.71). Compared to etanercept, tocilizumab had a higher risk of SI (HR 1.22, CI 1.02, 1.47) and certolizumab pegol a lower risk of SI (HR 0.75, CI 0.58, 0.97) in the fully adjusted model. The 30 day mortality following SI was 10.4% (95% CI: 9.2%, 11.6%).ConclusionsThe rate of SI was lower with certolizumab pegol than etanercept in the primary analysis but the result was no longer significant in several sensitivity analyses performed suggesting residual confounding may account for the observed difference. From these results it would be wrong to conclude that certolizumab pegol has a lower rate of SI than other biologics, however, the risk does not appear to be significantly higher as has previously been suggested. <br/

    Gender stratified adjustment of the DAS28-CRP improves inter-score agreement with the DAS28-ESR in rheumatoid arthritis

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    Objectives: To evaluate determinants of discordance between DAS28-ESR and DAS28-CRP and resulting impact on disease activity stratification in rheumatoid arthritis (RA)Methods: Paired DAS28-ESR and DAS28-CRP readings (n=31,074) were obtained from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA). Factors influencing discordance between DAS28-ESR and DAS28-CRP were evaluated alongside the resulting effect on disease activity stratification. The impact of gender adjustment to the DAS28-CRP was evaluated. Results: DAS28-CRP scores were ~0.3 lower than DAS28-ESR overall, with greatest differences for women (-0.35) and patients over 50 years old (-0.34). Mean male DAS28-CRP scores were 0.15 less than corresponding DAS28-ESR scores. Discordance between DAS28-ESR and DAS28-CRP significantly impacted disease activity stratification at low disease activity (LDA) and remission thresholds (32.0% and 66.6% concordance respectively). Adjusting DAS28-CRP scores by gender significantly (p &lt;0.001) improved agreement with the DAS28-ESR. Conclusion: Discordance between DAS28-ESR and DAS28-CRP is greatest for women and patients over 50 years of age, and influences disease activity stratification. The proposed gender adjusted DAS28-CRP improves inter-score agreement with DAS28-ESR, supporting more reliable disease activity stratification in treat-to-target approaches for RA

    Overlap of International League of Associations for Rheumatology and Preliminary Pediatric Rheumatology International Trials Organization Classification Criteria for Nonsystemic Juvenile Idiopathic Arthritis in an Established UK Multicentre Inception Cohort

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    Objective. The goal was to assess the degree of overlap between existing International League of Associations for Rheumatology (ILAR) and preliminary Paediatric Rheumatology International Trials Organisation (PRINTO) classification criteria for juvenile idiopathic arthritis (JIA). Methods. Participants from the Childhood Arthritis Prospective Study, a multicenter UK JIA inception cohort, were classified using the PRINTO and ILAR classification criteria into distinct categories. Systemic JIA was excluded because several classification items were not collected in this cohort. Adaptations to PRINTO criteria were required to apply to a UK health care setting, including limiting the number of blood biomarker tests required. The overlap between categories under the two systems was determined, and any differences in characteristics between groups were described. Results. A total of 1,223 children and young people with a physician’s diagnosis of JIA were included. Using PRINTO criteria, the majority of the patients had “other JIA” (69.5%). There was a high degree of overlap (91%) between the PRINTO enthesitis/spondylitis- and ILAR enthesitis-related JIA categories. The PRINTO rheumatoid factor (RF)–positive category was composed of 48% ILAR RF-positive polyarthritis and 52% undifferentiated JIA. The early-onset antinuclear antibodies–positive PRINTO category was largely composed of ILAR oligoarthritis (50%), RF-negative polyarthritis (24%), and undifferentiated JIA (23%). A few patients were unclassified under PRINTO (n = 3) and would previously have been classified as enthesitis-related JIA (n = 1) and undifferentiated JIA (n = 2) under ILAR. Conclusion. Under the preliminary PRINTO classification criteria for childhood arthritis, most children are not yet classified into a named category. These data can help support further delineation of the PRINTO criteria to ensure homogenous groups of children can be identified

    The impact of psoriasis on wellbeing and clinical outcomes in juvenile psoriatic arthritis

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    Objectives: Juvenile psoriatic arthritis (JPsA) has varied clinical features that are distinctive to other juvenile idiopathic arthritis (JIA) categories. This study investigates whether such features impact patient-reported and clinical outcomes. // Methods: Children and young people (CYP) were selected if recruited to the Childhood Arthritis Prospective Study, a UK multicentre JIA inception cohort, between January 2001 and March 2018. At diagnosis, patient/parent-reported outcomes (as age-appropriate) included the parental global assessment (10 cm VAS), functional ability (CHAQ), pain (10 cm VAS), health-related quality of life (CHQ psychosocial score), mood/depressive symptoms (MFQ) and parent psychosocial health (GHQ). Three-year outcome trajectories have previously been defined using active joint counts, physician and parent global assessments (PGA, PaGA respectively). Patient-reported outcomes and outcome trajectories were compared in i) CYP with JPsA versus other JIA categories, ii) CYP within JPsA, with and without psoriasis via multivariable linear regression. // Results: There were no significant differences in patient-reported outcomes at diagnosis between CYP with JPsA and non-JPsA. Within JPsA, those with psoriasis had more depressive symptoms (coefficient = 9.8, 95% CI = 0.5–19.0) than those without psoriasis at diagnosis. CYP with JPsA had 2.3 times the odds of persistent high PaGA than other ILAR categories, despite improving joint counts and PGA (95% CI 1.2, 4.6). // Conclusion: CYP with psoriasis at JPsA diagnosis report worse mood, supporting a greater disease impact in those with both skin and joint involvement. Multidisciplinary care with added focus to support wellbeing in children with JPsA plus psoriasis may help improve these outcomes

    Incidence and prevalence of juvenile idiopathic arthritis in the United Kingdom, 2000-2018: results from the Clinical Practice Research Datalink.

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    OBJECTIVE: The incidence and prevalence of JIA was last estimated in the UK in 1994. Since then the disease has been reclassified, the specialty of paediatric rheumatology has evolved and there has been a significant shift in disease management with new advanced therapies. This study aimed to provide up-to-date national estimates of this disease. METHODS: Children and young people (CYP) with JIA were identified in the Clinical Practice Research Datalink (CPRD) GOLD and Aurum databases, which source data from the two most commonly used primary care electronic health record systems in the UK. These databases were combined and the cohort was identified (2000-18) using predefined code lists. Validation was performed through linkage to the England Hospital Episode Statistics. Annual incidence and prevalence rates were calculated and stratified by gender, age group and nation of the UK. Direct standardization to the UK population was performed and 5 year incidence rates were calculated between 2003 and 2018. RESULTS: The age-standardized incidence rate was 5.61 per 100 000 population. The age-standardized prevalence rate in 2018 was 43.5 per 100 000. Rates were higher in Scotland compared with England: incidence rate ratio 1.27 (95% CI 1.11, 1.46). The 5 year incidence rates did not change significantly over time. CONCLUSIONS: This study has provided the first contemporaneous estimates of occurrence of JIA in the UK in 25 years. These data provide important estimates to inform resource allocation and health service development for management of JIA

    How to develop, externally validate, and update multinomial prediction models

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    Multinomial prediction models (MPMs) have a range of potential applications across healthcare where the primary outcome of interest has multiple nominal or ordinal categories. However, the application of MPMs is scarce, which may be due to the added methodological complexities that they bring. This article provides a guide of how to develop, externally validate, and update MPMs. Using a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis as an example, we outline guidance and recommendations for producing a clinical prediction model using multinomial logistic regression. This article is intended to supplement existing general guidance on prediction model research. This guide is split into three parts: 1) Outcome definition and variable selection, 2) Model development, and 3) Model evaluation (including performance assessment, internal and external validation, and model recalibration). We outline how to evaluate and interpret the predictive performance of MPMs. R code is provided. We recommend the application of MPMs in clinical settings where the prediction of a nominal polytomous outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation
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