ACR Open Rheumatology最新文献

The world population is aging, and the rheumatology workforce must be prepared to care for medically complex older adults. We can learn from our colleagues and experts in geriatrics about how to best manage multimorbidity, polypharmacy, geriatric syndromes, and shifting priorities of older adults in the context of delivering care for rheumatic and musculoskeletal diseases (RMDs). Polypharmacy, a common occurrence in an aging population with multimorbidity, affects half of older adults with RMDs and is associated with increased risk of morbidity and mortality. In addition, potentially inappropriate medications that should be avoided under most circumstances is common in the RMD population. In recent years, deprescribing, known as the process of tapering, stopping, discontinuing, or withdrawing drugs, has been introduced as an approach to improve appropriate medication use among older adults and the outcomes that are important to them. As the rheumatology patient population ages globally, it is imperative to understand the burden of polypharmacy and the potential of deprescribing to improve medication use in older adults with RMDs. We encourage the rheumatology community to implement geriatric principles, when possible, as we move toward becoming an age-friendly health care specialty.

Objective: Cartilage epigenetic changes are strongly associated with human osteoarthritis (OA). However, the influence of individual environmental OA risk factors on these epigenetic patterns has not been determined; herein we characterize cartilage DNA methylation patterns associated with aging and OA in a mouse model.

Methods: Murine knee cartilage DNA was extracted from healthy young (16-week, n = 6), old (82-week, n = 6), and young 4-week post-destabilization of the medial meniscus (DMM) OA (n = 6) C57BL6/J mice. Genome-wide DNA methylation patterns were determined via Illumina BeadChip. Gene set enrichment analysis was performed by Ingenuity Pathway Analysis. The top seven most differentially methylated positions (DMPs) were confirmed by pyrosequencing in an independent animal set. Results were compared to previously published human OA methylation data.

Results: Aging was associated with 20,940 DMPs, whereas OA was associated with 761 DMPs. Merging these two conditions revealed 279 shared DMPs. All demonstrated similar directionality and magnitude of change (Δβ 1.0% ± 0.2%, mean methylation change ± SEM). Shared DMPs were enriched in OA-associated pathways, including RhoA signaling (P = 1.57 × 10^-4 ), protein kinase A signaling (P = 3.38 × 10^-4 ), and NFAT signaling (P = 6.14 × 10^-4 ). Upstream regulators, including TET3 (P = 6.15 × 10^-4 ), immunoglobulin (P = 6.14 × 10^-4 ), and TLR7 (P = 7.53 × 10^-4 ), were also enriched. Pyrosequencing confirmed six of the seven top DMPs in an independent cohort.

Conclusion: Aging and early OA following DMM surgery induce similar DNA methylation changes within a murine OA model, suggesting that aging may induce pro-OA epigenetic "poising" within articular cartilage. Future research should focus on confirming and expanding these findings to other environmental OA risk factors, including obesity, as well as determining late OA changes in mice.

Objective: The optimal management of patients with incidentally found clinically isolated aortitis (CIA) after aneurysm repair is unclear. This study compared long-term surgical and clinical outcomes after surgical repair of thoracic aortic aneurysm between patients with CIA and patients with noninflammatory etiologies.

Methods: This is a matched cohort study. Patients with CIA were identified by histopathology following open thoracic aortic aneurysm repair. Two comparators without inflammation on pathology were matched to each patient by year of surgical repair. Outcomes included surgical complications, new vascular abnormalities on imaging, and death.

Results: One hundred sixty-two patients were included: 53 with CIA and 109 matched comparators. Median follow-up time was similar between groups (CIA 3.7 vs. comparator 3.3 years, P = 0.64). There was no difference in postoperative complications, surgical revision, or death between groups. Only 32% of patients with CIA saw a rheumatologist in the outpatient setting and 33% received immunosuppressive treatment. On surveillance imaging, no difference was seen in new or worsening aortic aneurysms, but there were significantly more vascular abnormalities in branch arteries of the thoracic aorta in patients with CIA (39% vs. 11%, P < 0.01).

Conclusion: Among patients who underwent surgical repair of a thoracic aortic aneurysm, patients with CIA were more likely than noninflammatory comparators to develop radiographic abnormalities in aortic branch arteries. Notably, there was no difference in risk of new aortic aneurysms or surgical complications despite most patients with CIA never receiving immunosuppression. This suggests that more selective initiation of immunosuppression in CIA may be considered after aortic aneurysm repair.

Objective: Features of multisystem inflammatory syndrome in children (MIS-C) overlap with other syndromes, making the diagnosis difficult for clinicians. We aimed to compare clinical differences between patients with and without clinical MIS-C diagnosis and develop a diagnostic prediction model to assist clinicians in identification of patients with MIS-C within the first 24 hours of hospital presentation.

Methods: A cohort of 127 patients (<21 years) were admitted to an academic children's hospital and evaluated for MIS-C. The primary outcome measure was MIS-C diagnosis at Vanderbilt University Medical Center. Clinical, laboratory, and cardiac features were extracted from the medical record, compared among groups, and selected a priori to identify candidate predictors. Final predictors were identified through a logistic regression model with bootstrapped backward selection in which only variables selected in more than 80% of 500 bootstraps were included in the final model.

Results: Of 127 children admitted to our hospital with concern for MIS-C, 45 were clinically diagnosed with MIS-C and 82 were diagnosed with alternative diagnoses. We found a model with four variables-the presence of hypotension and/or fluid resuscitation, abdominal pain, new rash, and the value of serum sodium-showed excellent discrimination (concordance index 0.91; 95% confidence interval: 0.85-0.96) and good calibration in identifying patients with MIS-C.

Conclusion: A diagnostic prediction model with early clinical and laboratory features shows excellent discrimination and may assist clinicians in distinguishing patients with MIS-C. This model will require external and prospective validation prior to widespread use.

Objective: Patient-reported outcome (PRO) data have assumed increasing importance in the care of patients with rheumatoid arthritis (RA), yet physician-derived disease activity measures, such as Clinical Disease Activity Index (CDAI), remain the most accepted metrics to assess disease activity. The possibility that newer longitudinal PRO data might be used as a proxy for the CDAI has not been evaluated.

Methods: Using data from a large pragmatic trial, we evaluated patients with RA initiating golimumab intravenous or infliximab. The classification target was low disease activity (LDA) (CDAI ≤10) at the first visit between months 3 and 12. Data were randomly partitioned into training (80%) and test (20%) data sets. Multiple machine learning (ML) methods (eg, random forests, gradient boosting, support vector machines) were used to classify CDAI disease activity category, conduct feature selection, and assess feature importance. Model performance evaluated cross-validated error, comparing different ML approaches using both training and test data.

Results: A total of 494 patients were analyzed, and 36.4% achieved LDA. The most important classification features included several Patient-Reported Outcomes Measurement Information System measures (social participation, pain interference, pain intensity, and physical function), patient global, and baseline CDAI. Among all ML methods, random forests performed best. Overall model accuracy and positive predictive values for all ML methods were approximately 80%.

Conclusion: ML methods coupled with longitudinal PRO data appear useful and can achieve reasonable accuracy in classifying LDA among patients starting a new biologic. This approach has promise for real-world evidence generation in the common circumstance when physician-derived disease activity data are not available yet PRO measures are.

Objective: The objective of this cross-sectional study was to investigate the impact of the COVID-19 pandemic on physical activity (PA) levels of patients with rheumatic and musculoskeletal diseases (RMDs) and to examine factors associated with decreased PA.

Methods: A sample of adult patients with RMDs (n = 7,776) was identified through electronic medical records from an academic health care system in North Carolina. Invitations to participate in an online survey were sent between July 2020 and September 2020 to assess self-reported changes in PA during the COVID-19 pandemic. Descriptive statistics, age-adjusted prevalence odds ratios (PORs), and 95% confidence intervals (CIs) were computed to examine patient characteristics associated with decreased PA.

Results: A total of 893 eligible participants completed the survey (mean age 57.8 ± 14.9 years, 75.8% female). The most common primary diagnoses reported among participants included rheumatoid arthritis (27.3%), osteoarthritis (16.0%), and systemic lupus erythematosus (SLE) (13.0%). More than half of participants (56.8%) reported engaging in less PA since the pandemic began. Factors associated with engaging in less PA included lower self-reported general health (POR, 2.21; CI, 1.64-2.97) and a diagnosis of SLE (POR, 1.57; CI, 1.03-2.38). Comorbidities associated with decreased PA included chronic pain (POR, 1.38; CI, 1.04-1.82), depression (POR, 1.48; CI, 1.09-2.01), and hypertension (POR, 1.44; CI, 1.10-1.90).

Conclusion: The COVID-19 pandemic has exacerbated barriers to PA in patients with RMDs. There is a critical need to provide resources, support, and multifaceted programs to encourage PA in patients with RMDs during the COVID-19 pandemic.

Objective: To assess the efficacy and tolerability of tocilizumab in a multicenter, randomized, double-blind, placebo-controlled trial in refractory adult patients with dermatomyositis (DM) and polymyositis (PM).

Methods: Thirty-six subjects with probable or definite DM/PM were enrolled in a 6-month phase 2B clinical trial and randomized 1:1 to receive tocilizumab (8 mg/kg intravenously) or placebo every 4 weeks for 24 weeks. Eligible subjects had either a DM rash, a myositis-associated autoantibody or an adjudicated PM diagnosis. Active disease was defined by at least three of six abnormal core set measures (CSMs), including a manual muscle testing (MMT)-8 score of less than 136/150. If the MMT-8 score was greater than 136, then a cutaneous score of 3 or more (10 cm visual analogue scale) was required along with three additional abnormal CSMs indicating disease activity. The primary endpoint compared the Total Improvement Score (TIS) between both arms from week 4 to 24. Secondary outcomes included time to meeting minimal TIS improvement, changes in CSMs, time to worsening, steroid-sparing effect, proportion of subjects meeting more stringent improvement criteria, and safety outcomes.

Results: There was no significant difference (P = 0.86) in the TIS over 24 weeks between tocilizumab and placebo arms. The secondary endpoints of time to improvement (minimal, moderate, or major), time to worsening, CSM changes, safety outcomes, and steroid-sparing effect were also not significantly different between arms.

Conclusion: Tocilizumab was safe and well tolerated but did not meet the primary or secondary efficacy outcomes in refractory DM and PM in this 24-week phase 2B study.