Arthritis Care & Research最新文献

Objective: Rheumatoid arthritis (RA) is associated with interstitial lung disease, bronchiectasis, rheumatoid lung nodules, and lung cancer. Recent guidelines proposed criteria for lung disease screening in RA, but the prevalence of abnormal lung findings in patients with RA is unknown.

Methods: Among all patients screened for lung cancer with low-dose chest computed tomography (CT) in the Mass General Brigham health care system between 2015 and 2023, we identified patients with and without RA. We compared the prevalence of lung nodules, "positive screen" (nodules requiring further imaging or biopsy), fibrotic lung changes, bronchiectasis, and lung cancer between patients with RA and comparators without RA using multivariable logistic regression.

Results: Among consecutive patients screened for lung cancer with clinically indicated low-dose chest CT, we identified 228 patients with RA and 14,805 comparators without RA. "Positive screens" were noted in 26.8% of patients with RA and 22.2% of patients without RA (P = 0.10). Lung cancer was found in 4.8% of patients with RA and 3.6% of patients without RA (P = 0.33). In multivariable models, RA was associated with positive screen (odds ratio [OR] 1.38, 95% confidence interval [CI] 1.02-1.87), fibrotic lung changes (OR 1.77, 95% CI 1.08-2.91), and bronchiectasis (OR 1.64, 95% CI 1.12-2.39).

Conclusion: Patients with RA had higher prevalence of positive screening, fibrotic changes, and bronchiectasis detected by low-dose chest CT performed for lung cancer screening. Approximately one in four patients with RA who met US Preventive Services Task Force lung cancer screening criteria had a positive screen, whereas 1 in 20 had lung cancer. These results emphasize the importance of lung cancer screening among eligible patients with RA and may inform screening strategies for other lung abnormalities.

Objective: We evaluated the ability of Renal Activity Index for Lupus (RAIL) to discriminate active lupus nephritis (LN) in adult patients with active systemic lupus erythematosus (SLE) and differentiate LN treatment response.

Methods: Urine samples from adults with biopsy-proven active Class III and IV LN from TULIP-LN (active-LN-group; NCT02547922) and adults with active, non-renal SLE from TULIP-1 (active-SLE-group; NCT02446912) were utilized and RAIL biomarkers (NGAL, KIM-1, MCP-1, adiponectin, hemopexin, ceruloplasmin) measured in the urine at baseline (both studies); and at Week 12 and Week 24 for TULIP-LN only. The groups were compared at baseline, and changes in RAIL-scores from baseline in the active-LN-group were compared between non-responders and responders over time, i.e., those with complete renal response (CRR), partial renal response (PRR) and urine protein-creatine ratio decrease ≥50% [UPCR50].

Results: At baseline, median [interquartile range (IQR)] concentrations of RAIL biomarkers were significantly higher (P<0.02) in the active-LN-group (n=128) versus the SLE-control-group (n=48), as were RAIL-scores [5.59 (4.31-6.47) versus 3.57 (2.78-4.47); P<0.001]. At Week 12/Week 24 there were 25/31 patients achieving CRR, 39/54 with PRR and 41/63 with UPCR50, respectively. Changes of RAIL-scores from baseline to Week 12/Week 24 significantly differed between non-responders and responders (PRR, CRR, UPCR50: all P<0.0006) with lower scores in responders. For CRR versus non-response, median [IQR] RAIL-scores decreased by -1.3 (-3.64/-0.21) versus -0.39 at Week 12, and -2.30 (-3.63/-1.03) versus -0.88 (-2.20/0.33) at Week 24, respectively.

Conclusions: The RAIL identifies active LN and longitudinally differentiates treatment response in adults with LN.

Objective: Patients and providers encounter challenges when conducting virtual musculoskeletal physical examinations (PEs) during rheumatology telehealth encounters. Guidance for a structured virtual PE could enhance the quality of clinical information gleaned and management decisions made during rheumatology telehealth visits. This study aims to build expert consensus and identify the most essential elements as the first step in defining the virtual rheumatology musculoskeletal PE.

Methods: A team with expertise in rheumatology telehealth, consisting of rheumatology attending physicians, educators, and a patient with rheumatic disease, conducted a modified Delphi to achieve consensus on the items determined to be most essential to the virtual rheumatology musculoskeletal PE. The modified Delphi consisted of two online surveys and a virtual meeting.

Results: The team identified seven items essential to the rheumatology musculoskeletal telehealth PE. These items describe elements in a focused joint examination as well as the assessment for level of activity of inflammatory arthritis. The modified Delphi method excluded maneuvers related to assessment of muscle strength and widespread pain syndromes, determining that these elements were better conducted in person.

Conclusion: A list of PE items most essential to rheumatology musculoskeletal telehealth encounters, supported by expert opinion and established evidence, marks the first step toward standardizing, evaluating, and teaching the virtual rheumatology PE. These items, alongside anticipated future revisions and improvements, promise to enhance the quality of telehealth care delivered to people with rheumatic diseases.

Objective: Estimates of polypharmacy among US adults with systemic lupus erythematosus (SLE)-a relatively young and disproportionately minoritized population-remain sparse. We sought to estimate the prevalence of polypharmacy in SLE and identify the most common medications used.

Methods: For this cross-sectional study, participants were recruited from a population-based cohort of adults with validated SLE in Atlanta, Georgia. Prescription and over-the-counter (OTC) medications were self-reported at the study visit. Polypharmacy was defined as five or more prescription or OTC medications. Estimates of polypharmacy prevalence by key sociodemographic and SLE-related participant characteristics were obtained using crude logistic regression and postestimation marginals.

Results: More than half (56.3%) of participants (n = 451; 15.3% ≥60 years old, 91.8% women, and 81.8% Black) reported polypharmacy. Older age (68.1%, 59.8%, and 43.0% for ages ≥60 years, 40-59 years, and 18-39 years), higher vs lower disease activity (65.8% vs 46.2%) and cumulative SLE-related damage (68.5% vs 42.4%), longer disease duration (62.4% vs 50.0%), and taking three to five vs zero to one immunomodulating medications (79.6% vs 38.0%) were associated with higher age-adjusted prevalence of polypharmacy; prevalence was not statistically significantly different by sex, race, or education. Although hydroxychloroquine (71.4%), glucocorticoids (44.3%), and other immunomodulating drugs (50.3%) were common, polypharmacy was most often driven by other medications, such as antihypertensives (61.9%), nonopioid pain relievers (51.7%), allergy treatments (22.4%), antidepressants (22.2%), and gastric reflux medications (21.7%).

Conclusion: Our results underscore the need to address the burden of medication regimens in this population through individualized medication optimization strategies that account for prescription and OTC medications used by those with SLE.

Objective: This cross-sectional study estimates national prevalence of arthritis-attributable work limitations (AAWL), describes differences in prevalence of AAWL by selected demographic and health characteristics, and assesses progress toward the AAWL Healthy People 2030 objective from 2019 to 2023.

Methods: Using data from the 2023 National Health Interview Survey, unadjusted and age-standardized prevalence of AAWL were estimated overall and by selected demographic and health characteristics. Data were age-standardized using four age groups (18-34, 35-44, 45-54, 55-64 years). t tests assessed differences by population subgroup and by data year (2019 and 2023). Age-standardized prevalence was reported when making comparisons.

Results: In 2023, the age-standardized prevalence of AAWL among US adults with arthritis aged 18 to 64 years was 38.8% (95% confidence interval 35.6-42.2). AAWL was significantly higher among Hispanic (50.1%) and non-Hispanic American Indian and Alaska Native adults (55.9%) compared to non-Hispanic White adults (35.6%). AAWL was higher among those with a disability (62.1%) than without (33.5%), those unable to work or who were disabled (61.8%) than employed/self-employed (32.2%), and veterans (52.5%) than nonveterans (37.8%). Respondents rating their health status as poor/fair (61.1%) or good (33.0%) had higher prevalence of AAWL than those with excellent/very good health status (23.9%). Adults with difficulty walking or climbing stairs (68.5%) had higher prevalence of AAWL compared to adults without difficulty (35.1%). The prevalence of AAWL during 2019 (38.1%) and 2023 (38.8%) was not significantly different.

Conclusion: More than one-third of US adults aged 18 to 64 years with arthritis report AAWL. Promoting arthritis-appropriate evidence-based interventions is important for achieving the Healthy People 2030 AAWL objective.

Objective: Poor self-reported sleep is common in rheumatoid arthritis (RA), but studies using objective sleep measures are rare. We report the prevalence of objectively measured sleep characteristics in individuals with RA and their association with clinical measures of RA disease activity.

Methods: Data were from a longitudinal study with four measurement periods at 6-month intervals. Sleep data were collected by actigraphy over 7 days at each period. Primary actigraphy sleep variables were sleep efficiency (SE; time asleep/time in bed), time awake after sleep onset (WASO), and total sleep time. At baseline, WatchPAT devices were also used for two nights to assess presence of sleep-disordered breathing (SDB; primarily obstructive sleep apnea (OSA) indicated by the apnea-hypopnea index [AHI]). Disease activity measures (Clinical Disease Activity Index [CDAI], Disease Activity Score [DAS] 28 joints, DAS C-reactive protein [CRP], and DAS erythrocyte sedimentation rate) were completed by rheumatologists before each sleep-monitoring period. Prevalence of sleep problems was estimated, and associations of sleep characteristics with RA disease activity were analyzed with regression models.

Results: Of 133 individuals enrolled, 116 had sufficient actigraph wear time for scoring at baseline, and 63 completed WatchPAT monitoring. More than 40% of the cohort had poor SE (<85%) at all measurement periods completed. More than half with WatchPAT assessments had moderate to severe OSA (AHI ≥ 15). Lower SE and higher WASO were associated with greater disease activity across all measures. AHI was associated with CDAI and DAS-CRP.

Conclusion: Objectively measured sleep problems were frequent and associated with RA disease activity. Given high rates of sleep disorders and their significant negative health effects, greater attention to sleep disorders among individuals with RA is warranted.

Objective: We aimed to test the efficacy of personalized treatment of older Veterans with chronic low back pain (CLBP) delivered by Aging Back Clinics (ABC) as compared with usual care (UC).

Methods: Two hundred ninety-nine Veterans age 65-89 with CLBP from 3 VA medical centers underwent baseline testing, randomization to ABC or UC and 12 months follow-up. ABC care was guided by trained physicians and published algorithms targeting key conditions contributing to CLBP (e.g., hip osteoarthritis, depression, fibromyalgia). UC was guided by the participant's primary care provider. The primary outcome was 6-month change in the Oswestry Disability Index (ODI). Among multiple secondary outcomes were pain intensity, quality of life (PROMIS-Global Health), self-reported physical function (PROMIS-29), falls, life space, and health care utilization collected at 3, 6, 9, and 12 months. Analyses were conducted according to intention-to-treat.

Results: There were no significant group differences in ODI change. Greater improvement with ABC in the PROMIS-29 physical function scale was observed at 12 months (1.7 vs -0.4 points), PROMIS Global physical health at 6 (1.3 vs -1.2) and 12 months (0.7 vs -1.5), PROMIS Global mental health at 6 months (0.2 vs -2.3), present and prior week average/worst pain over 12 months (all p<0.05). There were marginally significantly fewer falls over 12 months (p=0.0527).

Conclusions: We did not find confirmatory evidence that personalized care (ABC) was superior with respect to ODI. We did find preliminary evidence that ABC was superior in other respects including improved self-reported physical health, less pain and fewer falls.

Objective: This study aims to assess whether the 10-item Patient-Reported Outcomes Measurement Information System Scale v1.2-Global Health (PROMIS-GH) is useful to assess Global Mental Health (GMH) and Global Physical Health (GPH) in individuals with musculoskeletal disorders.

Methods: PROMIS-GH was administered to 4295 individuals (mean±SD age 56±14y, 70% female, chronic musculoskeletal pain [n=1142], rheumatoid arthritis [n=1987], hip/knee osteoarthritis [n=418], undergoing physiotherapy [n=947]). We investigated the legitimacy of calculating a GMH and a GPH subscale score (Confirmatory Factor Analyses) and of converting raw ordinal subscale scores to interval scores (checking IRT-assumptions [unidimensionality, local independence, monotonicity] and Graded Response Model fit), the ability of the subscales to discriminate different levels of health (items' discrimination parameters α), to cover relevant range of GMH and GPH (range of difficulty parameters β), their precision (item- and subscale-level information), and to compare demographical and clinical subgroups (Differential Item Functioning [DIF]).

Results: It is legitimate to calculate a GMH and a GPH subscale score (CFI=0.98/0.97, TLI=0.97/0.95, RMSEA=0.15/0.19, SRMR=0.05/0.07), and to convert raw scores to interval scores (IRT-assumptions met and model fit). Discrimination (items' α≥2), coverage (lowest β≤-2) and precision (reliability≥0.70 for large portions of the health domain) were adequate for all items, except item Global10, underscoring that they can distinguish individuals with different levels of GMH and GPH with precision. The subscales can be used to compare demographical and clinical subgroups (no DIF).

Conclusion: The PROMIS-GH can be used to measure GMH and GPH in individuals with musculoskeletal disorders. Replacement or improvement of item Global10 could be considered.

Worldwide, gout is increasing at a rapid rate. Although genetics and diet play primary roles in gout development, emerging evidence suggests that environmental risk factors may also play a significant contributory role. In this review, we examine the evidence linking environmental exposures to gout risk, summarize potential pathophysiologic mechanisms, and highlight key knowledge gaps and underexplored areas. In particular, we highlight the impact of air pollution, ambient temperature, water contamination with heavy metals, chronic kidney disease (as it relates both to gout and climate change), dietary factors such as ultraprocessed food consumption, and various other pollutants in increasing the risk of gout.