Arthritis Care & Research最新文献

Objective: There is an urgent need to identify clinical markers that can help physicians determine when additional treatment is necessary to manage the symptoms of knee osteoarthritis (OA). Patterns of physical activity that occur within a day, e.g., low activity in the morning and/or evening, may be a novel means to identify treatment need, given that symptoms may reduce daily activity at specific times of the day. The purpose of this study is to explore the relationship between within-day patterns of physical activity and all-cause mortality in adults with or at high risk for knee OA.

Methods: We performed a secondary analysis of the Osteoarthritis Initiative (NCT00080171). Our exposure was within-day patterns of physical activity using a Multidimensional (14-hour) Multilevel (4-day) Functional Principal Component Analysis to analyze accelerometer data from analytic baseline. The outcome was all-cause mortality assessed up to 8 years. Kaplan-Meier survival curves and Cox Proportional Hazards regressions were used to calculate adjusted Hazard Ratios (aHR).

Results: There were 1927 adults with or at high risk for knee OA included in this analysis. We identified 4 primary within-day activity patterns accounting for ~82% of sample variability. Participants who demonstrated low levels of activity in the morning and evening had 2.09 times the risk of mortality compared to those demonstrating the average activity pattern of the sample (aHR 2.09, 95% CI [1.15, 3.80]).

Conclusion: Unique within-day patterns of physical activity were associated with mortality risk. Those with inactivity in the morning and evening were at increased risk for mortality.

Objective: Knee osteoarthritis (OA) commonly affects individuals with Type 2 diabetes (T2DM) and is associated with increased risk of diabetes-related complications. To better understand potential mechanisms, we examined the association between symptomatic knee OA and glycemic control in individuals with T2DM.

Methods: In this cross-sectional study, we recruited individuals with T2DM aged ≥45 years from three academic centres in Canada. Online questionnaires assessed demographics, medical history and joint symptoms. We abstracted HbA1c from clinic records. Knee OA was defined as fulfilling NICE criteria. Target glycemic control was defined as a HbA1c ≤7.0%. Multivariable logistic regression assessed the association between knee OA and target glycemic control, adjusting for age, gender, education level, and body mass index (BMI). Secondary analyses assessed associations between knee OA with pain ≥20/100, and knee OA with walking difficulty, with target glycemic control.

Results: Among 351 participants (mean age 66.2 years, 50.7% women), 28.5% met criteria for knee OA and 43.9% were at glycemic target. In unadjusted analyses, those with knee OA had lower odds of being at target glycemic control (OR 0.60, 95% CI 0.37 to 0.97), but the association was not statistically significant after adjusting for confounders (OR 0.65, 95% CI 0.39 to 1.08). In those with knee OA with pain ≥20/100, a negative association with target glycemic control was statistically significant in adjusted analysis (OR 0.58, 95% CI 0.34 to 0.99).

Conclusions: Individuals with T2DM and painful knee OA are less likely to be at glycemic target, increasing their risk of diabetes complications.

Objective: We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient-facing graphical interface designed for use during outpatient visits ("RA PRO dashboard"). Initial qualitative studies showed positive perceptions from patients; here we assessed the effect of the RA PRO dashboard on patient decision-making, self-efficacy in symptom management, medication beliefs, and medication adherence in a randomized pragmatic trial.

Methods: We conducted an open cohort, stepped-wedge cluster-randomized trial at a single academic rheumatology clinic between February 2020 and August 2023. Rheumatology clinicians were randomized as clinician-patient clusters into four intervention sequences (5 time periods). Primary outcome measures derived from patient questionnaires: 4-item SURE scale of decisional conflict; PROMIS-SE 4a - Self-Efficacy for Managing Symptoms; Beliefs about Medicines Questionnaire - Specific Necessity-Concerns differential; and medication adherence. Generalized estimating equations models were used to evaluate the effect of clinician access to the dashboard on each outcome.

Results: 23 clinicians were included in the analysis. 554 patients completed 1083 study visits, of which 664 were in the intervention group. Adoption of the RA PRO dashboard by clinicians was highly variable. We observed limited effects of the intervention on the outcomes.

Conclusions: This trial revealed no significant short-term effects of the RA PRO dashboard on measures of patient decision-making, self-efficacy, medication beliefs, or adherence. Despite prior qualitative work showing improvements in the care experience, this study suggests that the dashboard's impact on traditional behavioral outcomes, at least in the short term, is limited.

Objective: The objectives of this study were (1) to estimate the 2023 prevalence of arthritis-attributable activity limitations (AAAL) among US adults with arthritis overall and by selected sociodemographic and health characteristics and (2) to assess progress toward the Healthy People 2030 objective to reduce AAAL by describing the change in AAAL prevalence from 2019 to 2023.

Methods: Cross-sectional data from the 2019 and 2023 National Health Interview Survey were used. Unadjusted and age-standardized prevalences of AAAL in 2023 were estimated overall and by sociodemographic and health characteristics among US adults who reported having doctor-diagnosed arthritis. Differences in the prevalence of AAAL by sociodemographic and health characteristics were assessed using t-tests. The overall unadjusted and age-standardized prevalences of AAAL in 2019 were estimated and tested against 2023 estimates for differences by survey year (2019 and 2023).

Results: During 2023, an estimated 24.8 million adults with arthritis reported having an activity limitation (age-adjusted prevalence, 47.8%; 95% confidence interval [CI], 45.0%-50.7%). There were significant differences in age-adjusted AAAL prevalence by sociodemographic and health characteristics. Although there was a decline in the estimated age-adjusted prevalence of AAAL from 2019 (49.2%; 95% CI, 46.7%-51.6%) to 2023, this change was not statistically significant.

Conclusion: About half of US adults with doctor-diagnosed arthritis report activity limitations due to arthritis, and the Healthy People 2030 objective for an AAAL prevalence of 46.8% has not been met. Organizations, working individually or as partners, to implement arthritis-appropriate evidence-based interventions could contribute to achieving the Healthy People 2030 goal of reducing AAAL among US adults with arthritis.

Objective: The objective of this study was to estimate the prevalence of arthritis among adults in the United States by nonmedical factors that influence health-adverse measures of social determinants of health (SDOH) and health-related social needs (HRSN).

Methods: Using 2022 Behavioral Risk Factor Surveillance System data, age-specific arthritis prevalences were estimated for 11 adverse SDOH/HRSN measures and a cumulative adverse SDOH/HRSN index, controlling for relevant covariates.

Results: Arthritis prevalence was higher among adults with adverse SDOH/HRSN compared to adults without adverse SDOH/HRSN. Arthritis prevalence increased as the number of adverse SDOH/HRSN increased.

Conclusion: Modifying or supplementing arthritis-appropriate, evidence-based self-management education programs to address SDOH/HRSN might improve arthritis management and outcomes.

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.