Arthritis Care & Research最新文献

Objective: The aim of this study was to systematically review the literature and undertake a meta-analysis to describe joint hypermobility, measured by Beighton score, in the general adult population and identify appropriate cut-offs to use for screening for GJH.

Methods: We searched AMED, OVID Medline, Embase and CINAHL, from inception to June 2024. We included studies reporting Beighton score in the general population, adults ≥18years. We extracted country of data collection, age, sex and Beighton score data. Data were grouped into participant ages. Meta-analysis was performed when 2 or more studies were available. We considered 2.5% prevalence as an indicator for typical variance.

Results: There were 46 studies (n=23,000) with 38 studies included in the age group analysis and 45 studies included in the analysis by sex. Overall, GJH prevalence at ≥6/9 was 2% (95%CI=0.6 to 6.6) and considered the threshold score indicating GJH outside typical variance. Sub-analysis of age groups indicated a prevalence of 5.0% (95%CI=1.2 to 18.0) at ≥6 for young adults (18-25 years). For adults (26-65years), ≥5 indicated a prevalence of 1.5% (95%CI=0.2 to 9.5), and older adults (≥65 years) had a prevalence of 6.5% (95%CI=5.5 to 7.7) at ≥4, higher than the threshold, but there was insufficient data for meta-analysis of ≥5. There were no statistically significant differences between female and male subjects at any cut-off (all p≥0.17).

Conclusion: Beighton score cut-offs should vary with age. For adults 18-25 years, 26-65 years and >65 years Beighton score cut-offs to screen for GJH should be ≥6/9, ≥5/9 and ≥4/9 respectively.

Objective: The purpose was to evaluate a biomarker score consisting of MUC5B rs35705950 promoter variant, plasma matrix metalloproteinase (MMP)-7, and serum anti-malondialdehyde-acetaldehyde (anti-MAA) antibody for RA-associated interstitial lung disease risk stratification.

Methods: Using a multicenter cohort of US veterans with RA, we performed a cross-sectional study of prevalent RA-ILD and cohort study of incident RA-ILD. Medical records were used to confirm clinical ILD diagnoses by chest imaging or lung biopsy pathology reports. A combined three biomarker score (range 0-3) was calculated based on the presence of MUC5B variant and elevations (upper 25% vs lower 75%) in MMP-7 and anti-MAA antibody concentrations. Multivariate logistic and Cox regression models were adjusted for clinical risk factors.

Results: Among 2,043 participants with RA, prevalent ILD was identified in 88 (88.7% male, mean age 63.6 years). The odds of prevalent RA-ILD were higher with increased biomarker score (aOR 12.21 [3.82, 38.97] for score of 3 vs. 0). A score ≥1 had 76.1% sensitivity, but 48.6% specificity. Incident RA-ILD developed in 148 participants, with those having a combined biomarker score of 3 having the highest risk (aHR 4.36 [1.55, 12.27]). Area under the curve for prevalent RA-ILD and Harrel's C for incident RA-ILD were highest when clinical risk factors were combined with the biomarker score.

Conclusion: This three-analyte biomarker score was associated with both prevalent and incident RA-ILD, improving risk stratification beyond clinical risk factors. While this score is inadequate for clinical implementation, these findings demonstrate the potential for biomarker scores in RA-ILD risk stratification.

Aims: We investigated whether a diagnosis of rheumatoid arthritis (RA) affects the quality of inpatient acute myocardial infarction (AMI) care and long-term mortality post-AMI.

Methods: We analysed data from 784,091 adults, 6,047 with a diagnosis of RA, from England and Wales hospitalised with AMI between 2005 and 2019 from the MINAP registry, linked with ONS mortality data and Hospital Episode Statistics. Cox regression models were used to compare risk of all-cause mortality at different time-points according to presence of RA.

Results: There was no difference in adjusted thirty-day mortality between groups (adjusted Hazard Ratio (aHR): 1.09, 95% CI 0.99-1.19, P=0.075). Beyond this, at one-year (aHR: 1.14, 95% CI 1.07-1.21), five-years, (aHR: 1.28, 95% CI 1.23-1.33) and to the end of the study period (aHR: 1.31, 95% CI 1.26-1.36), the risk of all-cause mortality was significantly higher in patients with RA (all P<0.001). Risk of cardiovascular mortality was not significantly different at thirty-days, or one-year (aHR: 1.08, 95% CI 1.00-1.17, P=0.058), but was at five-years (aHR: 1.15, 95% CI 1.08-1.23, P<0.001) and to the study endpoint post AMI (aHR: 1.18, 95% CI 1.11-1.24, P<0.001).

Conclusion: We found no meaningful disparities in inpatient care according to the presence of RA, however, those with RA have elevated long-term all-cause mortality post-AMI. Our findings suggest that the mortality burden of RA post-AMI is not driven by the quality of AMI care during admission and is likely driven by the progressive nature of the comorbidities and the complications of treatments associated with RA.

Objective: Our objective was to determine the feasibility and acceptability of the Treatment and Education Approach for Childhood-onset Lupus (TEACH), a six-session cognitive behavioral intervention addressing depressive, fatigue, and pain symptoms, delivered remotely to individual youth with lupus by a trained interventionist. We expected that TEACH would be considered feasible and acceptable based on recruitment and retention rates. We also examined the effect of TEACH on youths' depressive, fatigue, and pain symptoms compared to medical treatment as usual (TAU).

Methods: A pilot two-arm longitudinal randomized controlled clinical trial was conducted. Adolescents (12-17 years) and young adults (18-22 years) with childhood-onset systemic lupus erythematosus (cSLE) and elevated depressive, fatigue, and/or pain symptoms were recruited from six pediatric rheumatology sites across the United States and Canada from August 2020-March 2023.  Participants were randomized 1:1 to TEACH + TAU or TAU-alone and reported symptom data at baseline and eight weeks later.

Results: Of the 200 youth approached, 97 consented to participate (48.5% recruitment). Among 64 eligible participants, 32 were randomized to TEACH + TAU, and 32 to TAU-alone. Retention was high (92.2%). At post-assessment, the intervention group demonstrated reductions in depressive (7.88 [3.20, 12.60]; 14%) and fatigue (3.91 [0.44, 7.39]; 7%) symptoms, but not pain (0.89 [-0.06, 1.84]).

Conclusion: This remotely delivered cognitive behavioral intervention tailored to youth with lupus was feasible and associated with reduced depressive and fatigue symptoms compared with medical treatment as usual. Further increasing accessibility by implementing TEACH in medical settings may improve uptake and patient outcomes.

Objectives: Chronic lung disease is a potentially life-threatening complication of systemic juvenile idiopathic arthritis (SJIA-LD). However, its natural history, etiology, and effective management are unclear. We aimed to describe the baseline characteristics and biomarker profiles of the Childhood Arthritis and Rheumatology Research Alliance (CARRA) Registry SJIA-LD cohort.

Methods: Baseline data from all CARRA Registry patients with SJIA and those with SJIA-LD were included and analyzed. Lung disease-specific data were obtained using a standardized case report form through REDCap Cloud. Plasma biomarker profiles were determined using a custom Luminex panel.

Results: A total of 37 patients with SJIA-LD from 16 CARRA sites were identified and compared to all 928 patients with SJIA without known LD in the CARRA Registry. Patients with SJIA-LD were significantly younger at disease onset and were more likely to be of Asian descent. A higher medication burden was also found. Patients with SJIA-LD had higher levels of multiple lung injury biomarkers, cytokines, and chemokines compared to patients with both active and inactive SJIA without LD and healthy controls. Cluster analysis proposed three groups of SJIA-LD patients with distinct biomarker patterns reflecting differences in proinflammatory cytokines, type II chemokines, and markers of macrophage activation syndrome (MAS).

Conclusions: The CARRA SJIA-LD Cohort exhibits distinct clinical features, higher medication burden, frequent MAS, and plasma biomarker patterns specific to SJIA-LD compared to patients with SJIA without LD. A study is ongoing to assess more detailed clinical features, disease progression, patient-reported outcomes, and associated immune biomarkers.

Objective: Various epidemiological studies have shown the effects of certain foods on the risk of developing gout. However, the associations have not been comprehensively confirmed in the prospective studies.

Methods: Using data from 161,213 participants (54.32% Female) aged 56.5 years in a large cohort, we examined the association between the food intake of five major groups (grain, vegetables, fruits, meat, and dairy product) and gout. The diet diversity score (DDS) was calculated using five rounds of 24-hour dietary recall records. Genetic risk for gout was characterized using a weighted polygenic risk score (PRS). The International Classification of Diseases (ICD-10) code was used to identify gout cases. Cox proportional hazard models were used to estimate the hazard ratios (HRs) for each food group and the total DDS with the outcome of gout. Restricted cubic spline regressions were used to examine the dose-response association between DDS and gout risk.

Results: During a median follow-up of 13.26 years, 2,004 cases of gout occurred. After fully adjusting for confounders, intermediate (HR 0.798, 95% CI 0.707-0.900) and high (HR 0.807, 95% CI 0.701-0.928) DDS, as well as consumption of grain products (HR 0.852, 95% CI 0.789-0.919), dairy products (HR 0.889, 95% CI 0.846-0.933), vegetables (HR 0.960, 95% CI 0.927-0.993), and fruits (HR 0.924, 95% CI 0.879-0.972), were all associated with a reduced risk of gout (all P < 0.05). The protective association of dairy products with gout was particularly notable in the high genetic risk subgroup (HR 0.885, 95% CI 0.832-0.942; P=1.089×10^-4).

Conclusion: Our findings support dietary recommendations that advocate for moderately diverse diets rich in specific healthy foods as an effective measure for gout prevention, especially in high-risk populations.

Objective: People with rheumatic and musculoskeletal diseases (RMDs) can acquire disabilities that affect their participation in work and school. Generative artificial intelligence (genAI) may reduce documentation time, providing a tool for providers to efficiently write letters for accommodation and maximally advocate for people with disabilities. Therefore, fellows-in-training (FITs) must develop skills in disability advocacy and genAI for clinical care. We created an online module and simulation activity where FITs wrote letters for accommodation using genAI. We aimed to improve FITs' confidence implementing these skills and to identify areas needing further instruction.

Methods: FITs completed an online module and simulation activity engineering genAI prompts for letters for accommodation. Educators scored FITs' skills against a rubric. FITs measured their confidence conducting these skills in retrospective pre-post Likert scales that we compared with Wilcoxon signed-rank tests.

Results: Twenty-three FITs participated; 18 FITs (78.6%) completed the Likert scales. On average, FITs scored 83.83% against the rubric. The strongest skill was engineering genAI prompts (95.45%), and the lowest was differentiating the scope of practice between rheumatology providers and disabilities professionals coordinating accommodations (74.55%). FITs' confidence significantly improved across all objectives, most notably in engineering genAI prompts and finalizing letters drafted with genAI (p<0.0001).

Conclusion: Our educational materials teach FITs to incorporate genAI into writing letters for accommodation and addressing disparities from medical disabilities. Such skill development prepares FITs to enter the rheumatology workforce confident in their abilities integrating genAI into clinical activities and delivering care to people with disabilities from RMDs.

Objective: The purpose of this study was to determine the cumulative incidence of diagnosis switching, drug-free remission, and initiation of a biologic or targeted synthetic disease modifying anti-rheumatic drug (b/tsDMARD) in individuals with seronegative RA.

Methods: Adult residents of Olmsted County, MN with incident seronegative (rheumatoid factor-/anti-cyclic citrullinated peptide antibody-) RA meeting the 1987 and/or 2010 American College of Rheumatology classification criteria were included. Data were collected from 1/1/2005-12/31/2023 through manual chart review. Drug-free remission was defined as a period of ≥6 months where the individual was no longer on treatment for RA and did not have evidence of active inflammatory arthritis on evaluation by a rheumatologist. We calculated the 10-year cumulative incidence of a change in diagnosis, adjusting for the competing risk of death, and of drug-free remission and initiation of a b/tsDMARD, adjusting for the competing risks of death or change in diagnosis.

Results: A total of 176 individuals with seronegative RA (68% female) were included. The 10-year cumulative incidence of a change in diagnosis was 12.8% (95% confidence interval (CI): 8.7-18.9%). The most common change in diagnosis was to a spondyloarthritis (4%). The 10-year cumulative incidence of drug-free remission and initiation of a b/tsDMARD was 26.6% (95% CI: 20.7-34.2%) and 19.9% (95% CI: 14.7-26.9%), respectively. Over a median follow-up of 11.8 years, 49 individuals entered drug-free remission.

Conclusions: After initial diagnosis of seronegative RA, about 13% of individuals had a change in diagnosis and a quarter experienced drug-free remission within 10 years.