Advances in Rheumatology最新文献

Introduction: Neurocognitive changes may hinder the autonomy and independence of patients diagnosed with Systemic Lupus Erythematosus (SLE), being considered as one of the main negative outcomes.

Objectives: To evaluate the cognitive functions of patients diagnosed with active SLE and in remission at the Rheumatology Outpatient Clinic of the Hospital das Clínicas, School of Medicine, Universidade Federal de Goiás.

Methodology: This was a cross-sectional study, non-probabilistic sample and consecutive type. MiniMental State Examination (MMSE), Five Digits Test (FDT), and Rey's Complex Figures (FCR) were used in 83 patients in the period from November 2021 to May 2022. Patients with depressive disorders and users of alcohol and other drugs were excluded.

Results: The assessed cognitive functions - attention, memory, language, and executive functions - showed mild impairments, corroborating previous studies. There was a significant difference (p > 0,05) concerning cognitive performance when comparing patients with active SLE and in remission groups. Cognitive function was not associated with time of diagnosis.

Conclusion: In this sense, cognitive dysfunctions were present in patients with active SLE and remission. The results showed mild cognitive impairment in patients diagnosed with SLE.

Background: In rheumatoid arthritis (RA), the persistence of pain despite adequate inflammatory control may indicate the involvement of nociplastic and/or neuropathic pain (NP).

Objective: To investigate the presence of central sensitization (CS) and/or NP in patients with RA.

Methods: This descriptive, cross-sectional study used convenience sampling and included adult patients (≥ 18 years) with RA diagnosed according to the 2010 classification criteria of the American College of Rheumatology - ACR. The following instruments were applied: Central Sensitization Inventory (CSI), PainDETECT Questionnaire (PD-Q), and the 2010/2011 ACR criteria for fibromyalgia (FM), revised in 2016 (the latter applied only to patients with CSI ≥ 40). Descriptive and predictive analyses were conducted with a significance level of 5%.

Results: A total of 113 patients were included (mean age 54.3 years; 93.81% female; mean disease duration 12.11 years). CS was identified in 59.29% of patients; among them, 47.76% met FM criteria, while 52.24% did not. Among patients with CS, 53.73% reported severe pain (Numeric Rating Scale, NRS), and 44.78% had high disease activity (Clinical Disease Activity Index, CDAI). CSI was a significant predictor of both NRS and CDAI in univariate (p < 0.001) and multivariate analyses (p = 0.037 and p = 0.009, respectively). According to PD-Q, 17.70% of patients had probable NP, and PD-Q scores were predictive of CDAI, NRS, and CSI (p < 0.001). Among patients with CS, 26.87% also showed overlapping NP.

Conclusions: A substantial proportion of RA patients presented with CS-related pain, even in the absence of FM, suggesting a mixed ("top-down and bottom-up") nociplastic pain profile. Both CS and NP were associated with higher pain levels and increased disease activity. This is the first Brazilian study to characterize the coexistence of CS-related pain (with or without FM) and NP in RA, offering relevant insights for the clinical management of chronic pain in this population. These findings reinforce that routine screening and assessment of CS and NP should be an integral and continuous component of RA management, preceding therapeutic adjustments and extending throughout follow-up.

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