Classification of rheumatologic opinion on early inflammatory arthritis: harmonization of a heterogeneous standard

TNF receptor signalling in autoinflammatory diseases

Autoinflammatory syndromes are a group of disorders characterized by recurring episodes of inflammation as a result of specific defects in the innate immune system. Patients with autoinflammatory disease present with recurrent outbreaks of chronic systemic inflammation that are mediated by innate immune cells, for the most part. A number of these diseases arise from defects in the tumour necrosis factor receptor (TNFR) signalling pathway leading to elevated levels of inflammatory cytokines. Elucidation of the molecular mechanisms of these recently defined autoinflammatory diseases has led to a greater understanding of the mechanisms of action of key molecules involved in TNFR signalling, particularly those involved in ubiquitination, as found in haploinsufficiency of A20 (HA20), otulipenia/OTULIN-related autoinflammatory syndrome (ORAS) and linear ubiquitin chain assembly complex (LUBAC) deficiency. In this review, we also address other TNFR signalling disorders such as TNFR-associated periodic syndrome (TRAPS), RELA haploinsufficiency, RIPK1-associated immunodeficiency and autoinflammation, X-linked ectodermal dysplasia and immunodeficiency (X-EDA-ID) and we review the most recent advances surrounding these diseases and therapeutic approaches currently used to target these diseases. Finally, we explore therapeutic advances in TNF-related immune-based therapies and explore new approaches to target disease-specific modulation of autoinflammatory diseases.

A double-blind, randomized controlled trial to compare the effect of biannual peripheral magnetic resonance imaging, radiography and standard of care disease progression monitoring on pharmacotherapeutic escalation in rheumatoid and undifferentiated inflammatory arthritis: study protocol for a randomized controlled trial

Background

Permanent joint damage is a major consequence of rheumatoid arthritis (RA), the most common and destructive form of inflammatory arthritis. In aggressive disease, joint damage can occur within 6 months from symptom onset. Early, intensive treatment with conventional and biologic disease-modifying anti-rheumatic drugs (DMARDs) can delay the onset and progression of joint damage. The primary objective of the study is to investigate the value of magnetic resonance imaging (MRI) or radiography (X-ray) over standard of care as tools to guide DMARD treatment decision-making by rheumatologists for the care of RA.

Methods

A double-blind, randomized controlled trial has been designed. Rheumatoid and undifferentiated inflammatory arthritis patients will undergo an MRI and X-ray assessment every 6 months. Baseline adaptive randomization will be used to allocate participants to MRI, X-ray, or sham-intervention groups on a background of standard of care. Prognostic markers, treating physician, and baseline DMARD therapy will be used as intervention allocation parameters. The outcome measures in rheumatology RA MRI score and the van der Heijde-modified Sharp score will be used to evaluate the MRI and X-ray images, respectively. Radiologists will score anonymized images for all patients regardless of intervention allocation. Disease progression will be determined based on the study-specific, inter-rater smallest detectable difference. Allocation-dependent, intervention-concealed reports of positive or negative disease progression will be reported to the treating rheumatologist. Negative reports will be delivered for the sham-intervention group. Study-based radiology clinical reports will be provided to the treating rheumatologists for extra-study X-ray requisitions to limit patient radiation exposure as part of diagnostic imaging standard of care. DMARD treatment dose escalation and therapy changes will be measured to evaluate the primary objective. A sample size of 186 (62 per group) patients will be required to determine a 36% difference in pharmacological treatment escalation between the three groups with intermediate dispersion of data with 90% power at a 5% level of significance.

Discussion

This study will determine if monitoring RA and undifferentiated inflammatory arthritis patients using MRI and X-ray every 6 months over 2 years provides incremental evidence over standard of care to influence pharmacotherapeutic decision-making and ultimately hinder disease progression.

Trial registration

This trial has been registered at ClinicalTrials.gov: NCT00808496 (registered on 12 December 2008).

Validation of a Self-administered Inflammatory Arthritis Detection Tool for Rheumatology Triage

Objective.

The benefits of early intensive treatment of inflammatory arthritis (IA) are dependent on timely and accurate case identification. In our study, a scoring algorithm for a self-administered IA detection tool was developed and validated for the rheumatology triage clinical setting.

Methods.

A total of 143 consecutive consenting adults, newly referred to 2 outpatient rheumatology practices, completed the tool. A scoring algorithm was derived from the best-fit logistic regression model using age, sex, and responses to the 12 tool items as candidate predictors of the rheumatologists' blinded classification of IA. Bootstrapping was used to internally validate and refine the model.

Results.

The 30 IA cases were younger than the 113 non-cases (p < 0.0001) and included clinical diagnoses of early IA (n = 10), rheumatoid arthritis (n = 9), and spondyloarthropathies (n = 11). Non-cases included osteoarthritis (n = 46), pain syndromes (n = 19), systemic lupus erythematosus (n = 5), and miscellaneous, noninflammatory musculoskeletal complaints (n = 43). The best-fit model included younger age, male sex, “trouble making a fist,” “morning stiffness,” “ever told you have RA,” and “psoriasis diagnosis.” The overall predictive performance (standard error, SE) of the derivation model was 0.91 (0.03). Internal validation of the derivation model across 200 bootstrap samples resulted in a mean predictive performance (SE) of 0.904 (0.002). The refined tool had a mean predictive performance (SE) of 0.915 (0.002), a sensitivity of 0.855 (0.005), and specificity of 0.873 (0.003).

Conclusion.

A simple, self-administered tool was developed and internally validated for the sensitive and specific detection of IA in a rheumatology waiting list sample. The tool may be used to triage IA from rheumatology referrals.