Health Care Effect of Disease-Modifying Antirheumatic Drug Use on Patients with Rheumatoid Arthritis

Disease-Modifying Antirheumatic Drug Use and Its Effect on Long-term Opioid Use in Patients With Rheumatoid Arthritis

BACKGROUND/OBJECTIVES

The prevalence of chronic pain is high in patients with rheumatoid arthritis (RA), increasing the risk for opioid use. The objective of this study was to assess disease-modifying antirheumatic drug (DMARD) use and its effect on long-term opioid use in patients with RA.

METHODS

This cohort study included Medicare beneficiaries with diagnosis of RA who received at least 30-day consecutive prescription of opioids in 2017 (n = 23,608). The patients were grouped into non-DMARD and DMARD users, who were further subdivided into regimens set forth by the American College of Rheumatology. The outcome measured was long-term opioid use in 2018 defined as at least 90-day consecutive prescription of opioids. Dose and duration of opioid use were also assessed. A multivariable model identifying factors associated with non-DMARD use was also performed.

RESULTS

Compared with non-DMARD users, the odds of long-term opioid use were significantly lower among DMARD users (odds ratio, 0.89; 95% confidence interval, 0.83-0.95). All regimens except non-tumor necrosis factor biologic + methotrexate were associated with lower odds of long-term opioid use relative to non-DMARD users. The mean total morphine milligram equivalent, morphine milligram equivalent per day, and total days of opioid use were lower among DMARD users compared with non-DMARD users. Older age, male sex, Black race, psychiatric and medical comorbidities, and not being seen by a rheumatologist were significantly associated with non-DMARD use.

CONCLUSION

Disease-modifying antirheumatic drug use was associated with lower odds of long-term opioid use among RA patients with baseline opioid prescription. Factors associated with non-DMARD use represent a window of opportunity for intervention to improve pain-related quality of life in patients living with RA.

Influence of Multimorbidity on New Treatment Initiation and Achieving Target Disease Activity Thresholds in Active Rheumatoid Arthritis: A Cohort Study Using the Rheumatology Informatics System for Effectiveness Registry

OBJECTIVE

To determine whether multimorbidity is associated with treatment changes and achieving target disease activity thresholds in patients with active rheumatoid arthritis (RA).

METHODS

We conducted a retrospective cohort study of adults with active RA within the Rheumatology Informatics System for Effectiveness (RISE) registry. Multimorbidity was measured using RxRisk, a medication-based index of chronic disease. We used multivariable logistic regression models to assess the associations of multimorbidity with the odds of initiating a new disease-modifying antirheumatic drug (DMARD) in active RA, and among those initiating a new DMARD, the odds of achieving low disease activity or remission.

RESULTS

We identified 15,626 patients using the Routine Assessment of Patient Index Data 3 (RAPID3) cohort and 5,733 patients using the Clinical Disease Activity Index (CDAI) cohort. All patients had active RA, of which 1,558 (RAPID3) and 834 (CDAI) initiated a new DMARD and had follow-up disease activity measures. Patients were middle aged, female, and predominantly White, and on average received medications from 6 to 7 RxRisk categories. Multimorbidity was not associated with new DMARD initiation in active RA. However, a greater burden of multimorbidity was associated with lower odds of achieving treatment targets (per 1-unit RxRisk: RAPID3 cohort odds ratio [OR] 0.95 [95% confidence interval (95% CI) 0.91, 0.98]; CDAI cohort OR 0.94 [95% CI 0.90, 0.99]). Those with the highest burden of multimorbidity had the lowest odds of achieving target RA disease activity (RAPID3 cohort OR 0.54 [95% CI 0.34, 0.85]; CDAI cohort OR 0.65 [95% CI 0.37, 1.15]).

CONCLUSION

These findings from a large, real-world registry illustrate the potential impact of multimorbidity on treatment response and indicate that a more holistic management approach targeting multimorbidity may be needed to optimize RA disease control in these patients.

Identification and Validation of Hub Genes for Predicting Treatment Targets and Immune Landscape in Rheumatoid Arthritis

Background

Rheumatoid arthritis (RA) is recognized as a chronic inflammatory disease featured by pathological synovial inflammation. Currently, the underlying pathophysiological mechanisms of RA remain unclear. In the study, we attempted to explore the underlying mechanisms of RA and provide potential targets for the therapy of RA via bioinformatics analysis.

Methods

We downloaded four microarray datasets (GSE77298, GSE55235, GSE12021, and GSE55457) from the GEO database. Firstly, GSE77298 and GSE55457 were identified DEGs by the “limma” and “sva” packages of R software. Then, we performed GO, KEGG, and GSEA enrichment analyses to further analyze the function of DEGs. Hub genes were screened using LASSO analysis and SVM-RFE analysis. To further explore the differences of the expression of hub genes in healthy control and RA patient synovial tissues, we calculated the ROC curves and AUC. The expression levels of hub genes were verified in synovial tissues of normal and RA rats by qRT-PCR and western blot. Furthermore, the CIBERSORTx was implemented to assess the differences of infiltration in 22 immune cells between normal and RA synovial tissues. We explored the association between hub genes and infiltrating immune cells.

Results

CRTAM, CXCL13, and LRRC15 were identified as RA's potential hub genes by machine learning and LASSO algorithms. In addition, we verified the expression levels of three hub genes in the synovial tissue of normal and RA rats by PCR and western blot. Moreover, immune cell infiltration analysis showed that plasma cells, T follicular helper cells, M0 macrophages, M1 macrophages, and gamma delta T cells may be engaged in the development and progression of RA.

Conclusions

In brief, our study identified and validated that three hub genes CRTAM, CXCL13, and LRRC15 might involve in the pathological development of RA, which could provide novel perspectives for the diagnosis and treatment with RA.

Pain Management Strategies in Rheumatoid Arthritis: A Narrative Review

Rheumatoid Arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation and progressive deterioration of the joints, which generates pain and stiffness. The origin of this pain is multifactorial, with inflammation, secondary osteoarthritis, as well as central and peripheral sensitization playing important roles in the development. Up to 90.4% of RA patients visit a health professional for severe pain, and despite new therapies and sophisticated treatments, there are a limited number of options for analgesic management. We conducted a narrative review using the Medline and Pubmed search engines for articles in English and Spanish between 2000 and 2021, with the keywords "pain," "rheumatoid arthritis," "non-steroidal anti-inflammatory drugs" (NSAIDs), "opioids," "glucocorticoids," "disease modifying antirheumatic drugs" (DMARDs), "neuromodulators," "antidepressants," and "cannabinoids." The articles describing epidemiology, pathophysiological considerations and current treatments were selected after a screening process carried out by the authors. It was found that DMARDs are the fundamental basis of treatment, since the main mechanism of pain in this entity is inflammation. Nonetheless, a significant number of patients continue to have pain despite optimal treatment. The available evidence for pain management in RA is scarce, however, medications such as NSAIDs, topical capsaicin, weak opioids, and treatments such as joint infiltrations or surgical management, play an important role in its management. We believe more research efforts are needed to optimize analgesic treatment recommendations, however, based on the current existing evidence, we propose a stepwise algorithm in order to properly approach these cases.

Key PointsRA is a systemic autoimmune disease characterized by chronic inflammation, in which the main symptom is pain.Pain in RA is multifactorial, with inflammation, secondary osteoarthritis, as well as central and peripheral sensitization playing determining roles.DMARDs are the mainstay of RA treatment, although many patients continue to experience pain despite optimal management.Medications such as glucocorticoids, NSAIDs, topical capsaicin, and weak opioids are key elements when achieving analgesia in RA.Other pharmacological groups such as neuromodulators, antidepressants, muscle relaxants and cannabinoids currently do not have enough evidence to be recommended.

Methodological and reporting quality evaluation of meta-analyses on the Chinese herbal preparation Zheng Qing Feng Tong Ning for the treatment of rheumatoid arthritis

BACKGROUND

Zheng Qing Feng Tong Ning (ZQFTN) is a sinomenine (SIN) preparation that has been used in clinical practice. Our study aimed to assess the methodological and reporting quality of meta-analyses on the Chinese herbal formula ZQFTN for the treatment of rheumatoid arthritis (RA).

METHODS

Systematic searches were carried out with the 5 following electronic databases from inception to July 2019: China National Knowledge Infrastructure (CNKI), Wanfang, VIP database for Chinese technical periodicals (VIP), Cochrane Library and PubMed. The quality of the methodology and reporting was measured with the assessment of multiple systematic reviews 2 (AMSTAR 2) scale, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE).

RESULTS

Eight studies were identified. Among the 16 items of the AMSTAR 2 scale, four items were optimally reported ("Y" =100% of the items), and another four items were poorly reported ("Y" =0% of the items). Only 2 studies received a good overall score ("Y" ≥50% of the items). Regarding the PRISMA statement, the scores of 5 studies were lower than the average score (17.69), indicating that the quality of the reports was very low. In terms of the GRADE, none of the 61 results were of high quality (0.0%). Fifteen results were of medium quality (25%), 34 were of low quality (55%), and 12 were of very low quality (20%). Among the five downgrading factors, deviation risk (n = 61, 100%) was the most common downgrading factor, followed by inconsistency (n = 30, 50%), publication bias (n = 17, 28%), inaccuracy (n = 11, 18%) and indirectness (n = 0, 0%).

CONCLUSIONS

The methodological and reporting quality of the meta-analyses and systematic reviews in the included studies are less than optimal, and researchers should undergo additional training and follow the AMSTAR 2 scale, PRISMA statement and GRADE to design high-quality studies in the future.