Cluster analysis identifies unmet healthcare needs among patients with rheumatoid arthritis

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.

Determination of the factors associated with antigen-specific CD4+ T-cell responses to BNT162b2 in patients with rheumatoid arthritis

OBJECTIVES

Understanding interpatient variation in CD4+T-cell responses is the bases for understanding the pathogenesis and management of rheumatoid arthritis (RA). We examined immune responses to SARS-CoV-2 vaccine in a cohort of patients with RA and determined factors associated with the responses.

METHODS

Four hundred and thirty-one patients with RA having received two doses of BNT162b2, a messenger RNA-based vaccine for SARS-CoV-2, were included. Vaccine antigen-specific IgG was detected by ELISA, and antigen-specific CD4+T cells were detected by CD154 expression in response to antigenic stimulation. Expression of cytokines was concomitantly detected by intracellular staining. Associations among background variables, antigen-specific antibody production and the CD4+T-cell responses were analysed. Unsupervised hierarchical clustering was performed based on the profiles of antigen-specific cytokine production by CD4+T cells to stratify patients with RA.

RESULTS

Multivariate analysis indicated that ageing negatively affects CD4+T-cell response as well as antibody production. No association was detected between the presence or the levels of rheumatoid factor/anti-cyclic citrullinated peptide antibody and anti-vaccine immune responses. Methotrexate and prednisolone reduced B cell but not T-cell responses. Conventional immunophenotyping by the expression of chemokine receptors was not associated with the actual CD4+T-cell response, except for T helper cells (Th1). Functional immunophenotyping based on the profiles of antigen-specific cytokine production of CD4+T cells stratified patients with RA into three clusters, among which Th1-dominant type less frequently underwent joint surgery.

CONCLUSIONS

Clinical and immunological variables that are associated with antigen-specific CD4 T-cell responses in patients with RA were determined by analysing immune responses against SARS-CoV-2 vaccine.

Unsupervised cluster analysis of clinical and ultrasound features reveals unique gout subtypes: Results from the Egyptian College of Rheumatology (ECR)

BACKGROUND

Gout comprises a heterogeneous group of disorders; however, comorbidities have been the focus of most efforts to classify disease subgroups.

OBJECTIVES

We applied cluster analysis using musculoskeletal ultrasound (MSUS) combined with clinical and laboratory findings in patients with gout to identify disease phenotypes, and differences across clusters were investigated.

PATIENTS AND METHODS

Patients with gout who complied with the ACR/EULAR classification criteria were enrolled in the Egyptian College of Rheumatology (ECR)-MSUS Study Group, a multicenter study. Selected variables included demographic, clinical, and laboratory findings. MSUS scans assessed the bilateral knee and first metatarsophalangeal joints. We performed a K-mean cluster analysis and compared the features of each cluster.

RESULTS

425 patients, 267 (62.8 %) males, mean age 54.2 ± 10.3 years were included. Three distinct clusters were identified. Cluster 1 (n = 138, 32.5 %) has the lowest burden of the disease and a lower frequency of MSUS characteristics than the other clusters. Cluster 2 (n = 140, 32.9 %) was mostly women, with a low rate of urate-lowering treatment (ULT). Cluster 3 (n = 147, 34.6 %) has the highest disease burden and the greatest proportion of comorbidities. Significant MSUS variations were found between clusters 2 and 3: joint effusion (p < 0.0001; highest: cluster 3), power Doppler signal (p < 0.0001; highest: clusters 2), and aggregates of crystal deposition (p < 0.0001; highest: cluster 3).

CONCLUSION

Cluster analysis using MSUS findings identified three gout subgroups. People with more MSUS features were more likely to receive ULT. Treatment should be tailored according to the cluster and MSUS features.

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs

Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment response of b/tsDMARDs among clusters of RA patients identified by deep learning. We clustered RA patients clusters at first-time b/tsDMARD (cohort entry) in the Swiss Clinical Quality Management in Rheumatic Diseases registry (SCQM) [1999-2018]. We performed comparative effectiveness analyses of b/tsDMARDs (ref. adalimumab) using Cox proportional hazard regression. Within 15 months, we assessed b/tsDMARD stop due to non-response, and separately a ≥20% reduction in DAS28-esr as a response proxy. We validated results through stratified analyses according to most distinctive patient characteristics of clusters. Clusters comprised between 362 and 1481 patients (3516 unique patients). Stratified (validation) analyses confirmed comparative effectiveness results among clusters: Patients with ≥2 conventional synthetic DMARDs and prednisone at b/tsDMARD initiation, male patients, as well as patients with a lower disease burden responded better to tocilizumab than to adalimumab (hazard ratio [HR] 5.46, 95% confidence interval [CI] [1.76-16.94], and HR 8.44 [3.43-20.74], and HR 3.64 [2.04-6.49], respectively). Furthermore, seronegative women without use of prednisone at b/tsDMARD initiation as well as seropositive women with a higher disease burden and longer disease duration had a higher risk of non-response with golimumab (HR 2.36 [1.03-5.40] and HR 5.27 [2.10-13.21], respectively) than with adalimumab. Our results suggest that RA patient clusters identified by deep learning may have different responses to first-line b/tsDMARD. Thus, it may suggest optimal first-line b/tsDMARD for certain RA patients, which is a step forward towards personalizing treatment. However, further research in other cohorts is needed to verify our results.