Referring early arthritis patients within 6 weeks versus 12 weeks after symptom onset: an observational cohort study

Patients with rheumatoid arthritis presenting with mono- or oligo-arthritis and high VAS-ratings remain the most fatigued during 5 years of follow-up

OBJECTIVES

The severity of fatigue in RA has improved very little in recent decades, leaving a large unmet need. Fortunately, not all RA patients suffer from persistent fatigue, but the subgroup of patients who suffer the most is insufficiently recognizable at diagnosis. As disease activity is partly coupled to fatigue, DAS components may associate with the course of fatigue. We aimed to identify those RA patients who remain fatigued by studying DAS components at diagnosis in relation to the course of fatigue over a 5-year follow-up period in two independent early RA cohorts.

METHODS

In all, 1560 consecutive RA patients included in the Leiden Early Arthritis Cohort and 415 RA patients included in the tREACH trial were studied. Swollen joint count, tender joint count, ESR and Patient Global Assessment (PGA) [on a Visual Analogue Scale (VAS)] were studied in relation to fatigue (VAS, 0-100 mm) over a period of 5 years, using linear mixed models.

RESULTS

Higher tender joint count and higher PGA at diagnosis were associated with a more severe course of fatigue. Furthermore, patients with mono- or oligo-arthritis at diagnosis remained more fatigued. The swollen joint count, in contrast, showed an inverse association. An investigation of combinations of the aforementioned characteristics revealed that patients presenting with mono- or oligo-arthritis and PGA ≥ 50 remained the most fatigued over time (+20 mm vs polyarthritis with PGA < 50), while the DAS course over time did not differ. This subgroup comprised 14% of the early RA population. Data from the tREACH trial showed similar findings.

CONCLUSION

The RA patients who remain the most fatigued were those characterized by mono- or oligo-arthritis and high PGA (VAS ≥ 50) at diagnosis. This understanding may enable early-intervention with non-pharmacological approaches in dedicated patient groups.

Design of a Self-Measuring Device Based on Bioelectrical Impedance Analysis for Regular Monitoring of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic disease, in which permanent joint deformation is largely preventable with the timely introduction of appropriate treatment strategies. However, there is no consensus for patients with RA to monitor their progress and communicate it to the rheumatologist till the condition progresses to remission. In response to this unmet need, we proposed the design of a self-measuring device based on bioelectrical impedance analysis (BIA) for regular monitoring of inflammation levels. Twenty joints of both hands were measured to monitor trends in inflammation levels. Three electrodes were used to measure two joints of each finger. A central electrode was used for two consecutive measurements. A suitable form factor for the device was proposed for the vertical placement of the hand. To ensure the stability of measurements, an air cushion was incorporated into the back of the hand, hand containers were designed on both sides, and a mobile application was designed. We conducted a convergence-assessment experiment with five air pressures to validate the consistency and convergence of bioimpedance measurements. A heuristic evaluation of the usability around the product and mobile application was conducted in parallel by six subject matter experts and validated the design. This study underscores the significance of considering patients' disease activity during intervals between hospital visits and introduces a novel approach to self-RA care.

Predictors of inflammatory arthritis among new rheumatology referrals: a cross-sectional study

Objectives

Early diagnosis and treatment of inflammatory arthritis (IA) is essential to optimize disease control. We aimed to identify variables that distinguish IA from non-inflammatory arthropathy by performing a cross-sectional study of rheumatology referral letters and visit records. Further work describes time to assessment and documentation of variables within referral letters.

Methods

We reviewed rheumatology referral letters and new patient visits over a 6-month period. The diagnosis of IA was based on the clinical judgement of the assessing rheumatologist. IA diagnoses included RA, SpAs, unspecified IA, PMR, crystalline arthropathies and remitting seronegative symmetrical synovitis with pitting oedema. Univariate analysis was performed for each variable. Multivariable logistic regression was performed on statistically significant variables.

Results

Of 697 patients referred for arthralgia, 25.7% were diagnosed with IA. Variables predictive of IA included tenderness and swelling on examination and ≥1 h of morning stiffness. Increasing arthralgia duration, fatigue and brain fog were negative predictors. The median time from referral to IA diagnosis was 55 days and 20.7% of these patients were seen within 6 weeks. Among referral letters, documentation of arthralgia duration, morning stiffness or joint examination findings was uncommon (31%, 20.5% and 56.7%, respectively).

Conclusion

We identified positive and negative predictors of IA. Referral letters often missed key information required for the triaging process. Future efforts will be directed towards build a triaging tool to improve the referral quality and capture of those patients with IA who need earlier access to rheumatology care.

A machine learning model identifies patients in need of autoimmune disease testing using electronic health records

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.

Inducibility or predestination? Queries and concepts around drug-free remission in rheumatoid arthritis

INTRODUCTION

Drug-free remission (DFR) and its maintenance have been defined as the most desirable outcome for rheumatoid arthritis (RA) patients. DFR is linked to resolution of arthritis-related symptoms and restoration of normal functioning. However, there is currently no consensus if an optimal strategy, upon the initiation of treatment to the proper drugs withdrawal, is enough to induce it, or whether it is a predetermined condition related to patients' intrinsic characteristics.

AREAS COVERED

This review focuses on two key concepts around DFR. First, we analyze patients' intrinsic factors that may increase the chance of DFR, regardless of therapeutic choices. Second, we discuss on the evidence that it can be induced thanks to adequate, extrinsic disease management. Finally, we provide a glimpse into consequences of drugs discontinuation.

EXPERT OPINION

The early initiation of DMARD and the subsequent strict monitoring and drug adjustments are of primary importance to allow patients to achieve DFR, irrespective of initial treatment strategy. Once remission is obtained and maintained, it is possible to gradually taper and discontinue drugs with no dramatic consequences on the disease course. Among those who stop medication, ACPA-negative patients more often maintain the remission. Thus, DFR might depend on a combination of intrinsic and extrinsic factors.

Health disparities in rheumatoid arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by joint inflammation that involves symmetric polyarthritis of small and large joints. Autoimmune rheumatic diseases represent a significant socioeconomic burden as they are among the leading causes of death and morbidity due to increased risk of cardiovascular disease. Health disparities in patients with rheumatoid arthritis affect outcomes, prognosis, and management of the disease.

Positive impact on 10-year outcome of the window of opportunity for conventional synthetic DMARDs in rheumatoid arthritis: results from the ESPOIR cohort

Objective

This study aimed to assess the impact of disease-modifying antirheumatic drugs (DMARDs) on 10-year outcomes in rheumatoid arthritis (RA).

Methods

Patients with RA from the ESPOIR cohort with complete data on Disease Activity Score in 28 Joints (DAS28) and Health Assessment Questionnaire (HAQ) at 10 years (n=418) and complete radiographic data at baseline and 10 years (n=343) were included in this study. Outcomes were favourable outcome (FavOut) at 10 years, defined as DAS28 of <2.6 and HAQ score of <0.5 at 10 years, and absence of structural damage progression (AbsSDP) at 10 years, defined as change in Sharp-van der Heijde Score less than the smallest detectable change at 10 years (11.5 points). Three multivariate logistic regression models predicting 10-year outcome were built, considering (1) baseline variables only, (2) baseline variables and DMARD exposure (ever exposed, yes/no) and (3) baseline variables and DMARD exposure as weighted cumulative exposure (WCE) variables.

Results

Overall, 196/418 (46.9%) patients showed FavOut and 252/343 (73.5%) AbsSDP. WCE models had the best predictive performance, with area under the curve=0.80 (95% CI 0.74 to 0.87) for FavOut and 0.87 (95% CI 0.83 to 0.92) for AbsSDP. In the WCE model, the odds of FavOut and AbsSDP were reduced with conventional synthetic disease-modifying antirheumatic drug (csDMARD) initiation at 12 months versus at baseline (OR 0.78, 95% CI 0.65 to 0.94, and OR 0.89, 95% CI 0.76 to 0.98, respectively). Early biologics initiation was not significantly associated with either outcome.

Conclusions

WCE models can identify and quantify the long-term benefit of early csDMARD initiation on 10-year functional and structural outcomes in patients with RA.