Identification and Prediction of Fatigue Trajectories in People With Rheumatoid Arthritis

A cross-sectional study of multidimensional fatigue in biologic-treated rheumatoid arthritis: which variables play a role?

PURPOSE

Despite efficient biological disease-modifying antirheumatic drugs (bDMARDs) Rheumatoid Arthritis (RA) patients still suffer from high fatigue. This study aims to further our knowledge by assessing severity levels of the various fatigue dimensions and their associations with pain, sleep quality, and psychological well-being in bDMARDs treated RA patients.

MATERIAL AND METHODS

The sample consisted of 146 RA patients (84.9% females; mean age 56.6 ± 13.6 years), who completed the MFI-20, SF-36, PSQI, GAD-7 and PHQ-9. Correlation analyses and multiple linear regressions were used to analyse the data.

RESULTS

General fatigue was the highest reported type of fatigue, followed by physical fatigue dimensions. In the final regression model, pain and disability were significantly associated with physical fatigue (p ≤ 0.001, p ≤ 0.05, respectively) and reduced activity (p ≤ 0.01, p ≤ 0.05, respectively). Anxiety was significantly associated with mental fatigue (p ≤ 0.05) and reduced motivation (p ≤ 0.01). Regression analyses showed no significant associations between depression, sleep quality, and fatigue in any of the final models.

CONCLUSIONS

Our findings indicate that effectively addressing fatigue in RA patients requires an individualized approach. This approach should acknowledge the varying degrees of fatigue across different fatigue dimensions (physical or mental), while also taking into account the patient's mental health problems, pain levels, and disability levels.

Fatigue in rheumatoid arthritis patients: The status, independent risk factors, and consistency of multiple scales

INTRODUCTION

Fatigue is a common symptom that negatively affects the outcomes and functions of rheumatoid arthritis (RA) patients. This study aimed to assess the fatigue by two scales and validate their consistency, also to comprehensively evaluate fatigue-related risk factors in RA patients.

METHODS

In this case-control study, the fatigue of 160 RA patients and 60 healthy controls was evaluated by the Bristol Rheumatoid Arthritis Fatigue Multi-Dimensional Questionnaire (BRAF-MDQ) and the Chinese version of the Brief Fatigue Inventory (BFI-C). The 28-joint disease activity score using erythrocyte sedimentation rate of RA patients was assessed.

RESULTS

The BRAF-MDQ and BFI-C scores were elevated in RA patients versus healthy controls (all p < .001). Interestingly, BRAF-MDQ global fatigue score positively correlated with BFI-C global fatigue score in both RA patients (r = .669, p < .001) and healthy controls (r = .527, p < .001); meanwhile, Kendall's tau-b test showed a high consistency between BRAF-MDQ and BFI-C global fatigue scores in RA patients (W = 0.759, p < .001) and healthy controls (W = 0.933, p < .001). Notably, higher education level (В = -4.547; 95% confidence interval: -7.065, -2.029; p < .001) and swollen joint count (В = 1.965; 95% confidence interval: 1.375, 2.554; p < .001) independently related to BRAF-MDQ global fatigue score; higher education level (В = -0.613; 95% confidence interval: -0.956, -0.269; p = .001) and clinical disease activity index (В = 0.053; 95% confidence interval: 0.005, 0.102; p = .032) independently linked with BFI-C global fatigue score.

CONCLUSION

Fatigue commonly occurs in RA patients, which independently relates to education level and disease activity. Furthermore, BRAF-MDQ and BFI-C scales exhibit a high consistency in assessing fatigue.

Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.

Increased Confidence and Deeper Understanding of Fatigue Following Participation in Fatigue Education and Management Education in Systemic Sclerosis: A Mixed Methods Evaluation of a Virtual Intervention

OBJECTIVE

No fatigue-specific programs exist for people with systemic sclerosis (SSc) despite the burden of fatigue and negative impact on daily activities. This study used a convergent parallel mixed methods design to evaluate the impact of an adapted virtual intervention, Fatigue and Activity Management Education in Systemic Sclerosis (FAME-iSS), in the United States.

METHODS

Eighteen people with SSc participated in three separate six-week FAME-iSS programs. Participants completed the modified Fatigue Impact Scale (m-FIS), the Self-Efficacy for Performing Energy Conservation Strategies Assessment (SEPESCA), the Patient-Reported Outcomes Measurement Information System (PROMIS) Self-Efficacy for Managing Symptoms, and the Hospital Anxiety and Depression Scale (HADS) before, immediately after, and three months post intervention. Data were analyzed using descriptive and nonparametric inferential statistics. Participants' perceptions of the program and their use of fatigue management strategies were qualitatively analyzed using content analysis.

RESULTS

Eighty-nine percent of participants were women with a mean ± SD age of 52.0 ± 11.6 years and a mean ± SD disease duration of 13.7 ± 14.5 years, and more than 70% had a college degree. Significant improvements were observed for self-efficacy on the PROMIS Self-Efficacy for Managing Symptoms (P = 0.002) and SEPESCA (P = 0.016) immediately post intervention, which continued to significantly improve up to the three-month follow-up (P = 0.006 and 0.035, respectively). Significant improvements were also observed for the m-FIS between baseline and the three-month follow-up (P = 0.029). Participants reported a deeper understanding of fatigue and that they liked sharing strategies and experiences with each other along with the facilitator, citing that "there was a power in our group because we had a common condition."

CONCLUSION

FAME-iSS resulted in improvements in the impact of fatigue and self-efficacy for managing symptoms and performing energy conservation strategies. Feedback was positive, and the virtual format allowed for greater accessibility and sharing of strategies.

Baseline predictors of fatigue and persistent fatigue in rheumatoid arthritis: A longitudinal observational study

OBJECTIVES

To determine factors associated with fatigue in patients with rheumatoid arthritis (RA), and to identify baseline predictors of persistent fatigue at 12 months of follow-up.

METHODS

We enroled patients with RA fulfiling the 2010 American College of Rheumatology/European League Against Rheumatism criteria. Fatigue was assessed using the Arabic version of the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F). Using univariate and multivariate analyses, we examined baseline variables associated with fatigue and persistent fatigue (if the FACIT-F score was less than 40 at baseline and 12 months of follow-up).

RESULTS

We included 100 RA patients of whom 83% reported fatigue. At baseline, the FACIT-F score was significantly associated with older age (p = 0.007), pain (p < 0.001), global patient assessment (GPA) (p < 0.001), tender joint count (TJC) (p < 0.001), swollen joint count (p = 0.003), erythrocyte sedimentation rate (ESR) (p < 0.001), disease activity score (DAS28 ESR) (p < 0.001), and health assessment questionnaire (HAQ) (p < 0.001). At 12 months of follow-up, the percentage of patients who reported persistent fatigue was 60%. The FACIT-F score was significantly associated with age (p = 0.015), symptom duration (p = 0.002), pain (p < 0.001), GPA (p < 0.001), TJC (p < 0.001), C-Reactive Protein (p = 0.007), ESR (p = 0.009), DAS28 ESR (p < 0.001), and HAQ (p < 0.001). Pain was an independent baseline predictor of persistent fatigue (OR = 0.969 (95% CI [0.951-0.988]), p = 0.002).

CONCLUSIONS

Fatigue is a frequent symptom in RA. Pain, GPA, disease activity and disability were associated with fatigue and persistent fatigue. Baseline pain was the only independent predictor of persistent fatigue.

Longitudinal trajectories of fatigue in early RA: the role of inflammation, perceived disease impact and early treatment response

OBJECTIVE

Fatigue is common in rheumatoid arthritis (RA). We aimed to explore its longitudinal course, predictors and association with disease activity in early RA.

METHODS

Data came from the 2-year treat-to-target trial CareRA (Care in early RA) and its 3-year extension. Fatigue was measured on Visual Analogue Scale, Multidimensional Fatigue Inventory and Short Form-36 (SF-36) vitality. Longitudinal fatigue trajectories were identified with multivariate growth mixture modelling. Early predictors of fatigue and the association of fatigue and its trajectories with disease activity and clinical/psychosocial outcomes were studied with linear mixed models and multilevel mediation.

RESULTS

We included 356 and 244 patients in the 2-year and 5-year analyses, respectively. Four fatigue trajectories were identified: rapid, gradual, transient improvement and early deterioration, including 10%, 14%, 56% and 20% of patients. Worse pain, mental health and emotional functioning were seen in the early deterioration group. Higher pain, patient global assessment (PGA) and disability (Health Assessment Questionnaire), lower SF-36 mental components, and fewer swollen joints at baseline predicted higher fatigue over 5 years, while early disease remission strongly improved 5-year fatigue. The association between Simple Disease Activity Index and fatigue was mediated by PGA, pain, mental health and sleep quality.

CONCLUSIONS

Although fatigue evolves dynamically over time in early RA, most patients do not achieve sustained fatigue improvement despite intensive disease-modifying antirheumatic drug therapy. Higher 5-year fatigue levels were seen in patients with more perceived disease impact and fewer swollen joints at baseline. Conversely, early inflammatory disease control strongly improved long-term fatigue, pointing towards an early window of opportunity to prevent persistent fatigue.