Arthritis Care & Research最新文献

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.

Objective: This study aimed to identify metabolic signatures reflecting air pollution and further explore their associations with gout risk.

Methods: We retrieved 207,908 gout-free participants from a large cohort study. Annual average concentrations of fine particulate (PM_2.5), inhalable particulate matter (PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x) were estimated using bilinear interpolation based on the residential address. Elastic net regression was utilized to identify air pollutants-related metabolites and construct metabolic signatures. Cox regression models were performed to evaluate the association between air pollutants, related metabolic signatures, and the risk of gout. Mediation analyses were performed to explore how metabolites mediate the relationships linking air pollutants to gout.

Results: During a median follow-up of 12.56 years, 2474 incident gout cases were identified. We identified metabolite biomarkers reflecting air pollutants, including 87 metabolites for PM_2.5, 76 for PM₁₀, 68 for NO₂, and 71 for NO_x. Air pollution-related metabolic signatures were associated with elevated gout risk, with the hazard ratios (HRs) and 95% confidence intervals (CIs) of 1.10 (1.05, 1.14) for PM_2.5, 1.12 (1.08, 1.17) for PM₁₀, 1.09 (1.04, 1.13) for NO₂, and 1.11 (1.06, 1.15) for NO_x. Consistently, per IQR increase in PM_2.5, PM₁₀, NO₂, and NO_x was associated with 17%, 15%, 6%, and 6% increased gout risk, respectively. Additionally, metabolic signatures mediated the air pollutants-gout associations, with the mediation proportions varying from 2.36% for PM_2.5 to 4.30% for NO_x.

Conclusions: Our study indicated that air pollutants and metabolic signatures were associated with elevated gout risk, with metabolomic signatures playing a mediating role in air pollutants-gout relationship.

Objective: The objective of this study was to present effectiveness and tolerability of apremilast in a cohort of 21 patients with immune checkpoint inhibitor psoriatic arthritis (ICI-PsA) and/or immune checkpoint inhibitor psoriasis (ICI-PsO).

Methods: This multicenter study combined data from patients treated with apremilast after experiencing ICI-PsO and/or ICI-PsA. Patients taking apremilast before ICI initiation and patients with preexisting autoimmune disease before ICI therapy were also included. Response to apremilast was determined as complete, partial, or none as determined by improvement in Common Terminology Criteria for Adverse Events grading after drug initiation.

Results: There were 21 patients who used apremilast for either ICI-PsO and/or ICI-PsA, but only five of these patients had de novo ICI-PsO and/or ICI-PsA. Of these five patients, four had partial response or improvement in their immune-related adverse event with apremilast, although there were intolerances in three of these patients. Of the 21 total patients, 16 had a relevant preexisting autoimmune disease, indicating a likely flare of the underlying disease with ICI therapy. Flares occurred much sooner for patients with ICI-PsA (4 weeks) compared to patients with ICI-PsO only (39.7 weeks), although the majority of both groups had grade II severity. Among the 13 patients with preexisting disease and no exposure to apremilast before ICI therapy, all patients in the ICI-PsO-only group (100%) responded to apremilast with either a complete or partial response, whereas only 57% of patients in the ICI-PsA group had complete or partial response. Twenty-nine percent of patients in the entire cohort had to discontinue apremilast due to intolerability. Thirty-eight percent of the entire cohort had progression of cancer or death at last follow-up after being on apremilast.

Conclusion: This study highlights the potential benefit of apremilast for the treatment of ICI-PsO, both de novo and PsO flare, with less of an apparent benefit for ICI-PsA. Thirty percent of patients in the whole group had to discontinue apremilast due to intolerance. Apremilast may be an attractive therapeutic option for either condition given that it is not immunosuppressive, but further prospective observational studies with larger patient numbers are needed.

Objective: Several single-nucleotide polymorphisms (SNPs) have been associated with chronic pain syndromes. Our objective was to determine whether genetic variants are associated with pain and disease activity in rheumatoid arthritis (RA).

Methods: Participants were included from two independent RA cohorts: FORWARD (National Databank for Rheumatic Diseases, training data set) and the Veterans Affairs Rheumatoid Arthritis Registry (VARA; validation data set). Multivariable linear regression was used to estimate the relationship between cross-sectional pain scores and 36 fibromyalgia (FM)-associated SNPs in FORWARD. SNP alleles were summed and weighted by these regression coefficients to generate a genetic risk score (GRS) for pain for each participant in both cohorts. Linear regressions and generalized estimating equations were used to determine the relationship between this GRS, an existing pain intensity GRS, and pain and self-reported disease activity.

Results: The sample comprised 756 participants from FORWARD (mean age 56.8 years, 89.4% female) and 2,176 participants from VARA (mean age 64.3 years, 11.0% female) who had pain and genotyping data. Participants in the validation data set (VARA) with FM GRS in the highest quartile had more baseline pain than those in the lowest quartile (+0.55 [95% confidence interval 0.16-0.93], P = 0.006). This was also true for the existing pain intensity GRS. VARA participants in the highest quartile of both GRS had more pain throughout follow-up and higher disease activity scores.

Conclusion: GRS based on pain-related SNPs were associated with RA pain and disease activity, suggesting that the genetic risk of pain may have clinical impacts in RA, such as the likelihood of achieving remission.