Advances in Rheumatology最新文献

Background: Rheumatoid arthritis is a chronic autoimmune disorder characterized by joint destruction and inflammation. Persistent pain is a common symptom, often impacting quality of life, physical function, and mental health. Despite its prevalence, limited guidance exists for managing chronic rheumatoid arthritis-related pain. This systematic review and meta-analysis aimed to evaluate the effectiveness of various analgesic therapies for managing rheumatoid arthritis-related pain symptoms.

Methods: A comprehensive search of PubMed, EMBASE, Cochrane, and ClinicalTrials.gov was conducted from database inception through November 7, 2024. Eligible studies included randomized controlled trials and prospective cohorts reporting pain outcomes in adult rheumatoid arthritis patients. Pain score data was stratified by scale (5-point, 10-point, 100-point) and analyzed using a random-effects model.

Results: Twenty-six studies covering 52 treatment regimens were included. Etodolac demonstrated significant pain reduction on a 5-point scale (200 mg: 3.24; 95% CI: 2.86 to 3.63; p < 0.001; 300 mg: 3.35; 95% CI: 2.96 to 3.74; p = 0.00). Piroxicam also showed benefit (20 mg: 3.35; 95% CI: 2.91 to 3.78; p < 0.001). On the 10-point scale, only extracorporeal shock wave therapy significantly reduced pain (3.36; 95% CI: 2.25 to 4.48; p < 0.001). Celecoxib (Celbesta) (200 mg BID) was most effective in the 100-point subgroup (1.73; 95% CI: 1.32 to 2.15; p < 0.001). Heterogeneity was high (I² = 91.1%, 94.1%, 60.2%; all p < 0.05), and publication bias was present.

Conclusion: Several therapies demonstrated substantial pain relief, but heterogeneity, publication bias, and varying study quality limit generalizability. High-quality, long-term trials with standardized pain assessments are needed to guide personalized rheumatoid arthritis pain management.

Clinical trial number: PROSPERO CRD42024593971, 4 October 2024.

Background: Chronic fatigue severely compromises the quality of life in patients with granulomatosis with polyangiitis (GPA). Traditional diagnostic methods are often time-consuming, relying on clinical expertise and detailed questionnaires. This study aimed to develop a machine learning model capable of predicting chronic fatigue in GPA patients based on clinical data, with a particular focus on improving diagnostic capacity in regions with limited access to specialists.

Methods: This cross-sectional study collected data on fatigue (measured by the Modified Fatigue Impact Scale, MFIS), functional ability (Health Assessment Questionnaire, HAQ), disease activity (Birmingham Vasculitis Activity Score, BVAS), comorbidities, medication use, physical activity (International Physical Activity Questionnaire - Short Form, IPAQ-SF), and demographic characteristics. Four machine learning algorithms-logistic regression, decision tree, random forest, and extreme gradient boosting-were assessed using a 70/30 train-test split. Model performance was evaluated using area under the curve (AUC), accuracy, F1 score, recall, and precision. Statistical comparisons were performed using Welch's t-test and the Wilcoxon-Mann-Whitney U test for continuous variables, while the chi-square test or Fisher's exact test was applied to categorical variables, with significance set at P < 0.05. All analyses were conducted using R version 4.4.1 for Windows.

Results: Forty-five patients were assessed: 62.2% were female, with a median BMI of 27.72 kg/m² (23.2-30.1), a median age of 55.5 years, and a median disease duration of 12.0 years (6.0-17.0). Fatigue was reported by 20 patients (MFIS score ≥ 38), and seven patients (15.5%) had active disease according to the BVAS, which was similar between the fatigued and no fatigued groups (P > 0.05). The fatigued group had more acute-phase reactants and prednisone use (P < 0.05). The tree-based models achieved an AUC of approximately 0.80, outperforming the other models.

Conclusion: Tree-based models demonstrated superior predictive performance in identifying chronic fatigue. The Random Forest model, in particular, highlighted higher disability in activities of daily living (HAQ), older age, and longer disease duration as key predictors. Although the models performed well, additional data and incorporation of clinically relevant variables may further enhance predictive accuracy. Patients with GPA who experienced fatigue showed higher glucocorticoid use and elevated acute-phase reactants, despite similar levels of disease activity, suggesting mechanisms beyond inflammation. Machine learning shows strong potential as a clinical tool for fatigue identification, especially in settings with limited access to specialist care.

Trial registration: Universal Trial Number (UTN): U1111-1271-6003; Brazilian Clinical Trials Registry (ReBEC): RB

Purpose: To investigate the current status of self-perceived burden (SPB) among rheumatoid arthritis (RA) patients and analyze its influencing factors.

Methods: A cross-sectional study was conducted with 140 RA patients recruited from four tertiary hospitals in Taiyuan city, Shanxi Province. The data was collected through structured questionnaires assessing: General Information Questionnaire, Self-Perceived Burden Scale (SPBS), Activities of Daily Living (ADL) Scale, Social Support Rating Scale(SSRS).Multiple linear regression were employed to identify key predictors of SPB.

Results: From 112 distributed questionnaires, 104 were returned valid, with a 92.85% response rate. RA patients (40.38%) exhibited a moderate SPB, with a mean score of 29.10 ± 10.27. The primary concern was the emotional impact on caregivers, with the highest scores reflecting worry about caregiver burden (3.33±1.47). Multivariate linear regression analysis identified marital status (β = 0.19, P = 0.028), Activities of Daily Living (β=-0.26, P = 0.006), and family monthly income (β=-0.30, P = 0.002) as significant predictors of SPB in RA patients.

Conclusions: The findings underscore the moderate but significant SPB in RA patients, with key influences being Activities of Daily Living, marital status, and economic factors. These insights are vital for tailoring supportive care and shaping public health strategies to better serve this patient population.

Clinical trial registration number: Not applicable.

Background: Chikungunya fever (CF) is marked by acute, subacute, and chronic phases, with a significant proportion of patients experiencing persistent joint and neuropathic pain. These symptoms may mimic those of rheumatic diseases such as rheumatoid arthritis (RA) and spondyloarthritis (SpA). There is currently no consensus on whether the infection directly causes chronic joint disease or serves as an immunological trigger for the development of rheumatic conditions.

Aim: This study aims to evaluate the overall proportion of CF patients who progress to chronic arthropathy, and to identify how many of these patients meet the classification criteria for RA and SpA (including ankylosing spondylitis (AS) and psoriatic arthritis (PsA)).

Methods: A thorough search was conducted in electronic databases, including PubMed, Embase, LILACS, and the Cochrane Library. The primary endpoint was the occurrence of chronic arthropathy, defined as joint signs and symptoms lasting more than six weeks following the acute phase of CF. The secondary endpoint involved the proportion of patients meeting the classification criteria for RA, SpA, AS, and PsA. A random-effects meta-analysis model was utilized to combine studies and determine the pooled frequency of persistent joint symptoms. Subgroup analyses were performed based on the fulfillment of classification criteria for rheumatic diseases. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tools. The study protocol was registered with PROSPERO (CRD42020211430).

Results: A total of thirty-eight studies, comprising data from 12.524 individuals with CF published between 2008 and 2022, met the inclusion criteria. Of these, 4.324 (34.5%) patients developed chronic arthropathy; among them, 11.43% (240 in 2099) patients met the criteria for RA, 12.1% (86 in 711) patients for SpA, 3.42% (36 in 1052) patients for AS, and 2.05% (25 in 1220) patients for PsA.

Conclusion: This study found that approximately one-third of CF patients experience persistent joint pain lasting over six weeks. However, only a minority of these individuals meet classification criteria for rheumatic diseases.