5-EPIFAT trial protocol: a multi-center, randomized, placebo-controlled trial of the efficacy of pharmacotherapy for fatigue using methylphenidate, bupropion, ginseng, and amantadine in advanced cancer patients on active treatment.

BACKGROUND: Cancer-related fatigue (CRF) is still undertreated in most patients, as evidence for pharmacological treatments is limited and conflicting. Also, the efficacy of the pharmacological agents relative to each other is still unclear. Therefore, medications that may potentially contribute to improving CRF will be investigated in this head-to-head trial. Our main objective is to compare the efficacy of methylphenidate vs. bupropion vs. ginseng vs. amantadine vs. placebo in patients with advanced cancer. METHODS: The 5-EPIFAT study is a 5-arm, randomized, multi-blind, placebo-controlled, multicenter trial that will use a parallel-group design with an equal allocation ratio comparing the efficacy and safety of four medications (Methylphenidate vs. Bupropion vs. Ginseng vs. Amantadine) versus placebo for management of CRF. We will recruit 255 adult patients with advanced cancer who experience fatigue intensity ≥ 4 based on a 0-10 scale. The study period includes a 4-week intervention and a 4-week follow-up with repeated measurements over time. The primary outcome is the cancer-related fatigue level over time, which will be measured by the functional assessment of chronic illness therapy-fatigue (FACIT-F) scale. To evaluate safety, the secondary outcome is the symptomatic adverse events, which will be assessed using the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events in cancer clinical trials (PRO-CTCAE). Also, a subgroup analysis based on a decision tree-based machine learning algorithm will be employed for the clinical prediction of different agents in homogeneous subgroups. DISCUSSION: The findings of the 5-EPIFAT trial could be helpful to guide clinical decision-making, personalization treatment approach, design of future trials, as well as the development of CRF management guidelines. TRIAL REGISTRATION: IRCT.ir IRCT20150302021307N6. Registered on 13 May 2023.

Predictors of quality of life, functional status, depression and fatigue in early arthritis: comparison between clinically suspect arthralgia, unclassified arthritis and rheumatoid arthritis.

BACKGROUND: Rheumatoid arthritis (RA) is often preceded by symptomatic phases during which classification criteria are not fulfilled. The health burden of these "at-risk" stages is not well described. This study assessed health-related quality of life (HRQoL), function, fatigue and depression in newly presenting patients with clinically suspect arthralgia (CSA), unclassified arthritis (UA) or RA. METHODS: Cross-sectional analysis of baseline Patient-Reported Outcome Measures (PROMs) was conducted in patients from the Birmingham Early Arthritis Cohort. HRQoL, function, depression and fatigue at presentation were assessed using EQ-5D, HAQ-DI, PHQ-9 and FACIT-F. PROMs were compared across CSA, UA and RA and with population averages from the HSE with descriptive statistics. Multivariate linear regression assessed associations between PROMs and clinical and sociodemographic variables. RESULTS: Of 838 patients included in the analysis, 484 had RA, 200 had CSA and 154 had UA. Patients with RA reported worse outcomes for all PROMs than those with CSA or UA. However, "mean EQ-5D utilities were 0.65 (95%CI: 0.61 to 0.69) in CSA, 0.61 (0.56 to 0.66) in UA and 0.47 (0.44 to 0.50) in RA, which was lower than in general and older (≥ 65 years) background populations." In patients with CSA or UA, HRQoL was comparable to chronic conditions such as heart failure, severe COPD or mild angina. Higher BMI and older age (≥ 60 years) predicted worse depression (PHQ-9: -2.47 (-3.85 to -1.09), P < 0.001) and fatigue (FACIT-F: 5.05 (2.37 to 7.73), P < 0.001). Women were more likely to report worse function (HAQ-DI: 0.13 (0.03 to 0.21), P = 0.01) and fatigue (FACIT-F: -3.64 (-5.59 to -1.70), P < 0.001), and residents of more deprived areas experienced decreased function (HAQ-DI: 0.23 (0.10 to 0.36), P = 0.001), greater depression (PHQ-9: 1.89 (0.59 to 3.18), P = 0.004) and fatigue (FACIT-F: -2.60 (-5.11 to 0.09), P = 0.04). After adjustments for confounding factors, diagnostic category was not associated with PROMs, but disease activity and polypharmacy were associated with poorer performance across all PROMs. CONCLUSIONS: Patient-reported outcomes were associated with disease activity and sociodemographic characteristics. Patients presenting with RA reported a higher health burden than those with CSA or UA, however HRQoL in the pre-RA groups was significantly lower than population averages.

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer.

OBJECTIVE: Cancer-related fatigue (CRF) has been considered the biggest influencing factor for cancer patients after surgery. This study aimed to develop and validate a nomogram for severe cancer-related fatigue (CRF) patients with cervical cancer (CC). METHODS: A cross-sectional study was conducted to develop and validate a nomogram (building set = 196; validation set = 88) in the Department of Obstetrics and Gynecology of a Class III hospital in Shenyang, Liaoning Province. We adopted the questionnaire method, including the Cancer Fatigue Scale (CFS), Medical Uncertainty in Illness Scale (MUIS), Medical Coping Modes Questionnaire (MCMQ), Multidimensional Scale of Perceived Social Support (MSPSS), and Sense of Coherence-13 (SOC-13). Binary logistic regression was used to test the risk factors of CRF. The R4.1.2 software was used to develop and validate the nomogram, including Bootstrap resampling method, the ability of Area Under Curve (AUC), Concordance Index (C-Index), Hosmer Lemeshow goodness of fit test, Receiver Operating Characteristic (ROC) curve, Calibration calibration curve, and Decision Curve Analysis curve (DCA). RESULTS: The regression equation was Logit(P) = 1.276-0.947 Monthly income + 0.989 Long-term passive smoking - 0.952 Physical exercise + 1.512 Diagnosis type + 1.040 Coping style - 0.726 Perceived Social Support - 2.350 Sense of Coherence. The C-Index of the nomogram was 0.921 (95% CI: 0.877∼0.958). The ROC curve showed the sensitivity of the nomogram was 0.821, the specificity was 0.900, and the accuracy was 0.857. AUC was 0.916 (95% CI: 0.876∼0.957). The calibration showed that the predicted probability of the nomogram fitted well with the actual probability. The DCA curve showed when the prediction probability was greater than about 10%, the benefit of the nomogram was positive. The results in the validation group were similar. CONCLUSION: This nomogram had good identifiability, accuracy and clinical practicality, and could be used as a prediction and evaluation tool for severe cases of clinical patients with CC.

Fatigue, depression, and sleep disorders are more prevalent in patients with metabolic-associated fatty liver diseases.

AIM: To determine the prevalence and risk factors for depression, sleep disturbances, and exhaustion in MAFLD patients. METHODS: Two hundred twenty-four consecutive patients with MAFLD attending the outpatient clinic from April to October 2023; were subjected to clinical evaluation, laboratory testing including non-invasive laboratory markers, fibroscan (measuring steatosis and fibrosis), and different quantitative and qualitative fatigue scores. A control group including 342 patients without MAFLD was taken. RESULTS: The prevalence of fatigue, depression, and sleeping disorders in the MAFLD group was 67.8%, 75%, 62.5% vs 21%, 16.4%, and 19.5% in the control group respectively ( P = <0.001, P = <0.001 and P = <0.001). MAFLD with fatigue was significantly associated with the presence and severity of steatosis and fibrosis by fibroscan ( P = <0.0001). By univariate and multivariate analysis: age, BMI, waist circumference, T2DM, hypertension, steatosis, fibrosis, and Fib-4 were considered risk factors for fatigue in the MAFLD group. The age, high social level, diabetes, hypertension, steatosis, fibrosis, and fib-4 were considered, by univariate and multivariate analysis, independent risk factors for depression in the MAFLD group. age, BMI, waist circumference, diabetes, hypertension, steatosis, fibrosis, and fib-4 were independent risk factors for sleep disorders in MAFLD. CONCLUSION: Fatigue, sleeping disorders, and depression are more prevalent in MAFLD patients than in the general population. The lower health utility scores in patients with MAFLD are associated with more advanced stages of the disease.

Effects of physical and psychological symptoms on cancer-related fatigue among esophageal cancer patients.

BACKGROUND: Cancer-related fatigue (CRF) is considered one of the most prevalent and distressing symptoms among cancer patients and may vary among patients with different cancer types. However, few studies have explored the influence of physical and psychological symptoms on CRF among esophageal cancer (EC) patients without esophagectomy. Therefore, this study aimed to examine the effects of physical and psychological symptoms on CRF among EC patients without esophagectomy. METHODS: In the present study, a cross-sectional study was conducted from February 2021 to March 2022 in Liaoning Province, China. Among the 112 included participants, 97 completed our investigation. The questionnaires used consisted of the Brief Fatigue Inventory (BFI), the MD Anderson Symptom Inventory Gastrointestinal Cancer Module (MDASI-GI), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), and demographic and clinical information. Multivariate linear regression was conducted to test the relationships between physical and psychological symptoms and CRF. RESULTS: Of the 97 EC patients, 60.8% reported CRF (BFI ≥ 4). The mean age of the participants was 64.92 years (SD = 8.67). According to the regression model, all the variables explained 74.5% of the variance in CRF. Regression analysis indicated that physical symptoms, including constipation, diarrhoea, and difficulty swallowing, contributed to CRF. On the other hand, depressive symptoms increased the level of CRF among EC patients without esophagectomy. CONCLUSIONS: Given the high prevalence of CRF among EC patients without esophagectomy, it is urgent to emphasize the importance of fatigue management interventions based on physical and psychological symptoms to alleviate CRF in EC patients.

Relationship between glycated hemoglobin levels at admission and chronic post-stroke fatigue in patients with acute ischemic stroke.

BACKGROUND: Chronic Post-Stroke Fatigue (PSF) is a common and persistent complications among ischemic stroke survivors. The serum glycated hemoglobin (HbA1c) level, as it is known has emerged as a critical risk factor for Acute Ischemic Stroke (AIS) and post-stroke cognitive and emotional impairment. However, no studies have been conducted on the link between HbA1c and PSF. Therefore, this study aims to estimate the relationship between HbA1c and PSF in the chronic phase. METHODS: A longitudinal study was conducted on 559 patients diagnosed with their first AIS episode and admitted to Suining Central Hospital within three days after onset. All patients were examined for serum HbA1c, blood glucose levels and routine blood biochemical indicators at admission. The Fatigue Severity Scale (FSS) was employed to assess fatigue symptoms at six months post-stroke. Multivariate logistic regression and smooth curve fitting were used to analyze the relationship between admission HbA1c, blood glucose levels, discharge blood glucose and PSF, and the predictive value of HbA1c on PSF was assessed using a segmented linear regression model. RESULTS: 189(33.8 %)of the 559 patients included in the study, reported PSF at six-month follow-up. Compared with the non-PSF group, the PSF group displayed significantly higher levels of HbA1c (7.8 ± 3.0 vs 6.5 ± 2.0 %, P < 0.001), admission blood glucose (7.8 ± 3.8 vs 7.1 ± 3.5 mmol/L, P = 0.041), and discharge blood glucose (6.3 ± 1.6 vs 5.8 ± 1.2 mmol/L, P < 0.001). The dose-response relationship among admission HbA1c, blood glucose, discharge blood glucose and PSF showed that HbA1c level is positively and non-linearly related to the risk of PSF. A linear positive correlation is noted between PSF and discharge blood glucose levels, while no significant correlation was observed for the blood glucose levels upon admission. CONCLUSIONS: Higher HbA1c levels at admission were independently associated with the risk of chronic PSF, the correlation between blood glucose and PSF showed significant variability, HbA1c may serve as a more stable risk factor in predicting the occurrence of chronic PSF and long-term active glycemic management may have a favorable impact on chronic PSF after AIS.

Measuring fatigue in people with multiple sclerosis - which questionnaire should be used? A Plain Language Summary of Publication.

WHAT IS THIS SUMMARY ABOUT?: This summary explains the findings of a recent study that compared different questionnaires used by doctors to measure levels of fatigue in people with multiple sclerosis (MS). The aim of the study was to find out which questionnaire doctors should use to measure fatigue in people with MS in the future. Fatigue, which can be described as the overwhelming feeling of tiredness or exhaustion, is a very common symptom of MS. For the majority of people with MS, fatigue is one of the worst symptoms of MS, so it is essential that doctors can measure it accurately. Currently, people with MS are asked to complete questionnaires so that their care team can see the effect of fatigue on their day-to-day lives. There are many questionnaires that are used to measure fatigue in people with MS. It would be valuable to come to an agreement, based on evidence from research like this study, on which questionnaire is the most appropriate for measuring fatigue in both research and healthcare settings. This study compared a questionnaire called the PROMIS® Fatigue (MS) 8a, referred to throughout this summary as the PROMIS® MS Fatigue Short Form, with two of the most commonly used questionnaires: the Fatigue Severity Scale (FSS) and the Modified Fatigue Impact Scale (MFIS). The questionnaires were compared to see which one should be recommended to doctors for measuring fatigue in people with MS. WHAT ARE THE KEY TAKEAWAYS?: It was found that while all three questionnaires were good, the PROMIS® MS Fatigue Short Form questionnaire was better than the other two questionnaires at showing differences in levels of fatigue between people with MS. The PROMIS® MS Fatigue Short Form was also found to be better than the Fatigue Severity Scale (FSS) at showing changes in the person with MS's level of fatigue. The PROMIS® MS Fatigue Short Form questionnaire may help people with MS to better communicate challenges with their fatigue to their doctors. WHAT WAS THE MAIN CONCLUSION REPORTED BY THE RESEARCHERS?: The study suggests that the PROMIS® MS Fatigue Short Form questionnaire is a helpful tool for doctors and people with MS to measure fatigue.

The association of fatigue severity with pain level, obesity indices and functional performances in women with knee osteoarthritis.

INTRODUCTION: Knee osteoarthritis is most common among women with obesity. It may lead to physical inactivity that, in turn, causes fatigue or lack of physical enthusiasm to perform meaningful daily activities. Hence, this study aimed to examine whether pain level, obesity indices and functional performances are associated with fatigue severity in women with knee osteoarthritis (KOA). MATERIALS AND METHODS: This cross-sectional study recruited women referred to physiotherapy to manage OA. The measurements included fatigue severity (fatigue severity scale); pain level (numerical rating scale); obesity indices (body mass index, fat %, waist circumference); functional performances (upper limb strength, lower limb strength, mobility, exercise capacity and quality of life). A simple linear regression analysis was used to determine which independent variable may be associated with fatigue severity. RESULTS: Ninety-six women with unilateral KOA participated in this study (Mean age, 55.70, Standard Deviation, SD 6.90) years; Mean fatigue severity, 34.51, SD 14.03). The simple linear regression analysis showed that pain level (ß=4.089, p<0.001), fat % (ß=0.825, p<0.001) and QoL (ß=0.304, p<0.001) were significantly associated with fatigue. After controlling for pain level, only fat % was significantly associated with fatigue (ß=0.581, p=0.005). CONCLUSION: Pain level, fat %, and QoL appear to be associated with fatigue severity in women with KOA. In addition, pain symptoms may interact with factors associated with fatigue severity.

Pathophysiology, Assessment, and Management of Post-Stroke Cognitive Impairment, Depression, and Fatigue.

Post-stroke cognitive impairment, depression, and fatigue are common, persistent, and disabling. This review summarizes current knowledge on the pathophysiology, assessment, and management of these debilitating neuropsychiatric sequelae of stroke. We briefly review evolving knowledge on the neural mechanisms and risk factors for each condition. We describe patient-reported outcome measures and clinician rating techniques that can be used to assist in screening and comprehensive assessment. We then discuss behavioral and pharmacologic management strategies. Heterogeneity of stroke remains a challenge in management and new research is still needed to optimize and personalize treatments for stroke survivors.

[Features of fibromyalgia in patients with rheumatoid arthritis]. / Osobennosti fibromialgii u patsientov s revmatoidnym artritom.

OBJECTIVE: To determine the features of fibromyalgia (FM) in patients with rheumatoid arthritis (RA). MATERIAL AND METHODS: Seventy-six patients participated in the study. The patients were divided into 2 groups: RA+FM (n=55), FM (n=21). Anamnesis of life and disease was carefully collected in all patients. The intensity (according to VAS) and phenotype (Pain DETECT, DN4) of pain syndrome (PS), the presence of symptoms of central sensitization (CSI), fatigue (FSS), signs of anxiety and depression (HADS), sleep quality (PSQI), cognitive functions (DSST) and quality of life (QoL) (EQ-5D, FIQR) were also evaluated. RESULTS: The average age of patients in the FM group was significantly lower (42 [35; 53] vs. 50 [42.5; 59], p=0.042). Patients with «pure¼ FM without RA were more often divorced and had no children (p=0.045 and p=0.02, respectively). The duration of PS in the groups did not differ (11 [7; 17] vs. 8 [5; 13] years, p=0.429), however, patients with «pure¼ FM waited longer for diagnosis (115 [40; 198] vs. 20 [5.5; 59] months, p<0.001), and they also were less likely to be recognized as disabled (p=0.003). Patients of both groups had equally severe fatigue, anxiety, depression, sleep disorders and cognitive functions compared to the norms. Patients of the FM group noted a lower QoL (according to EQ-5D, p=0.041) then in RA+FM group, despite the comparable severity of FM and the intensity of the PS in both groups. CONCLUSION: FM in patients with RA develops at a later age compared to «pure¼ FM. The clinical picture of FM with and without RA does not differ in the main manifestations, however, the QoL of patients with «pure¼ FM is lower. Accounting for fibromyalgia in the treatment of rheumatoid diseases can significantly improve the QoL of patients.

Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.

Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test. We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5-hydroxyindole-3-acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data-driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.

Fatigued but not sleepy? An empirical investigation of the differentiation between fatigue and sleepiness in sleep disorder patients in a cross-sectional study.

OBJECTIVE: Sleepiness and fatigue are common complaints among individuals with sleep disorders. The two concepts are often used interchangeably, causing difficulty with differential diagnosis and treatment decisions. The current study investigated sleep disorder patients to determine which factors best differentiated sleepiness from fatigue. METHODS: The study used a subset of participants from a multi-site study (n = 606), using a cross-sectional study design. We selected 60 variables associated with either sleepiness or fatigue, including demographic, mental health, and lifestyle factors, medical history, sleep questionnaires, rest-activity rhythms (actigraphy), polysomnographic (PSG) variables, and sleep diaries. Fatigue was measured with the Fatigue Severity Scale and sleepiness was measured with the Epworth Sleepiness Scale. A Random Forest machine learning approach was utilized for analysis. RESULTS: Participants' average age was 47.5 years (SD 14.0), 54.6% female, and the most common sleep disorder diagnosis was obstructive sleep apnea (67.4%). Sleepiness and fatigue were moderately correlated (r = 0.334). The model for fatigue (explained variance 49.5%) indicated depression was the strongest predictor (relative explained variance 42.7%), followed by insomnia severity (12.3%). The model for sleepiness (explained variance 17.9%), indicated insomnia symptoms was the strongest predictor (relative explained variance 17.6%). A post hoc receiver operating characteristic analysis indicated depression could be used to discriminate fatigue (AUC = 0.856) but not sleepiness (AUC = 0.643). CONCLUSIONS: The moderate correlation between fatigue and sleepiness supports previous literature that the two concepts are overlapping yet distinct. Importantly, depression played a more prominent role in characterizing fatigue than sleepiness, suggesting depression could be used to differentiate the two concepts.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder.

BACKGROUND AND OBJECTIVE: Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). METHODS: A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). RESULTS: The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. CONCLUSION: The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

Minimal clinically important differenceof fatigue severity scale in patients with chronic stroke.

BACKGROUND: One of the most prevalent symptoms of stroke is fatigue. Fatigue severity scale is the most often used tool for evaluating fatigue in stroke patients, its minimal clinically important difference threshold has not been determined. This study aimed to identify the minimal clinically important difference of fatigue severity scale in stroke patients. METHODS: All study participants were examined using fatigue severity scale and multidimensional fatigue symptom inventory-short form before and after the intervention. The 6-week intervention combined graded activity training and pacing therapy employed to reduce fatigue severity. Participants reported changes in their fatigue severity after the intervention with the global rating of change and visual analog scale. The minimal clinically important difference of the fatigue severity scale calculated using both anchor- and distribution-based methods. RESULTS: A total of 117 stroke patients were included in the study. Using multidimensional fatigue symptom inventory-short form, global rating of change, and visual analog scale as an anchor, the minimal clinically important difference of fatigue severity scale was obtained at 3.5, 4.5, and 4.5, respectively. The minimal clinically important difference for fatigue severity scale varied from 4.28 to 12.90 using the distribution-based method, with SEM = 4.28 displaying the best sensitivity and specificity for use as minimal clinically important difference. CONCLUSIONS: The minimal clinically important difference value for the fatigue severity scale was estimated at 3.5_12.90 using anchor-based and distribution-based methods. The study's results can be utilized to understand the effectiveness of fatigue interventions in stroke patients in clinical and research settings.

Why does malaise/fatigue occur? Underlying mechanisms and potential relevance to treatments in rheumatoid arthritis.

INTRODUCTION: Fatigue and malaise are commonly associated with a wide range of medical conditions, including rheumatoid arthritis (RA). Evidence suggests that fatigue and malaise can be overwhelming for patients, yet these symptoms remain inadequately-managed, largely due to an incomplete elucidation of the underlying causes. AREAS COVERED: In this assessment of the published literature relating to the pathogenesis of fatigue or malaise in chronic conditions, four key mechanistic themes were identified. Each theme (inflammation, hypothalamic-pituitary-adrenal axis, dysautonomia, and monoamines) is discussed, as well as the complex network of interconnections between themes which suggests a key role for inflammatory cytokines in the development and persistence of fatigue. EXPERT OPINION: Fatigue is multifaceted, poorly defined, and imperfectly comprehended. Moreover, the cause and severity of fatigue may change over time, as a consequence of the natural disease course or pharmacologic treatment. This detailed synthesis of available evidence permits us to identify avenues for current treatment optimization and future research, to improve the management of fatigue and malaise in RA. Within the development pipeline, several new anti-inflammatory therapies are currently under investigation, and we anticipate that the next five years will herald much-needed progress to reduce the debilitating nature of fatigue in patients with RA.

Evaluation of functionality-mobility in patients with skeletal dysplasias. Application of the STEMS tool ("everyday symptoms and mobility screening tool for skeletal dysplasias").

Individuals with differing forms of skeletal dysplasias (SD) frequently report impaired mobility and symptoms. With the objetive to evaluate mobility and associated symptoms in people with SD at an Argentinian pediatric hospital, using an Argentinian version of the Screening Tool for Everyday Mobility and Symptoms (STEMS), a simple questionnaire that allows clinicians to quickly identify the presence of symptoms associated with mobility in people with SD, while considering different environmental settings and the use of assistive devices, an analytical study of a consecutive sample of patients older than 5 years with SD and their affected relatives was carried out.Diagnosis, comorbidities, socioenvironmental, therapeutic, auxological and mobility variables were recorded. The presence and intensity of symptoms was noted through use of both the STEMS and validated scales. Descriptive, association and correlation analyzes were performed. One hundred and nineteen individuals with SD were enrolled in the study and divided into groups: Osteogenesis Imperfecta (OI, n = 55), Achondroplasia (ACH, n = 36) and Other SD resulting in disproportionate short stature (n = 28). Mobility assistive devices were almost exclusively used by individuals with OI. They were more frequently used by individuals with overweight and obesity, more severe form of the disease and in the outdoor settings. Two thirds (66.4%) of the individuals assessed in this study reported pain, 87.4% reported fatigue, and 58.8% reported both pain and fatigue. The intensity of symptoms was similar between groups and correlated with age and auxological variables. The STEMS was clear, easy and quick to use for identifying presence of pain and fatigue in this population group. The STEMS proved to be a simple and useful tool for evaluating functional mobility and associated symptoms in our population of individuals with SD.

Cardiac indices of driver fatigue across in-lab and on-road studies.

Driver fatigue is a major contributor to road accidents. Therefore, driver assistance systems (DAS) that would monitor drivers' states may contribute to road safety. Such monitoring can potentially be achieved with input from ECG indices (e.g., heart rate). We reviewed the empirical literature on responses of cardiac measures to driver fatigue and on detecting fatigue with cardiac indices and classification algorithms. We used meta-analytical methods to explore the pooled effect sizes of different cardiac indices of fatigue, their heterogeneity, and the consistency of their responses across studies. Our large pool of studies (N = 39) allowed us to stratify the results across on-road and simulator studies. We found that despite the large heterogeneity of the effect sizes between the studies, many indices had significant pooled effect sizes across the studies, and more frequently across the on-road studies. We also found that most indices showed consistent responses across both on-road and simulator studies. Regarding the detection accuracy, we found that even on-road classification could have been as accurate as 70% with only 2-min of data. However, we could only find two on-road studies that employed fatigue classification algorithms. Overall, our findings are encouraging with respect to the prospect of using cardiac measures for detecting driver fatigue. Yet, to fully explore this possibility, there is a need for additional on-road studies that would employ a similar set of cardiac indices and detection algorithms, a unified definition of fatigue, and additional levels of fatigue than the two fatigue vs alert states.

Between-Day Reliability of the Gait Characteristics and Their Changes During the 6-Minute Walking Test in People With Multiple Sclerosis.

BACKGROUND: Gait characteristics and their changes during the 6-minute walking test (6MWT) in people with multiple sclerosis (pwMS) have been described in the literature, which one may refer to as walking fatigability in the body function level of the International Classification of Functioning, Disability, and Health. However, whether these metrics are reliable is unknown. OBJECTIVE: To investigate the between-day reliability of the gait characteristics and their changes in pwMS and healthy controls (HCs). METHODS: Forty-nine pwMS (EDSS 4.82 ± 1.22 and 54.7 ± 9.36 years) and 23 HCs (50.6 ± 6.1 years) performed the 6MWT, as fast as possible but safely while wearing Inertial Measurement Units. Gait characteristics were measured in the pace, rhythm, variability, asymmetry, kinematics, coordination, and postural control domains and were obtained in intervals of 1 minute during the 6MWT. In addition, gait characteristics change in the last minute compared with the first minute were calculated for all gait variables using a fatigability index (ie, distance walking index). The intraclass correlation coefficient (ICC), Bland-Altman Plots, and Standard error of measurement were applied to investigate reliability. RESULTS: Reliability of gait characteristics, minute-by-minute, and for their changes (ie, using the fatigability index) ranged from poor to excellent (pwMS: ICC 0.46-0.96; HC: ICC 0.09-0.97 and pwMS: ICC 0-0.72; HC: ICC 0-0.77, respectively). CONCLUSION: Besides coordination, at least 1 variable of each gait domain showed an ICC of moderate or good reliability for gait characteristics changes in both pwMS and HC. These metrics can be incorporated into future clinical trials and research on walking fatigability.Clinical Trial Registration: NCT05412043.

Identification of Fatigue Subtypes and Their Correlates in Prevalent Heart Failure: A Secondary Analysis of the Atherosclerosis Risk in Communities Study.

BACKGROUND: Among patients with heart failure (HF), fatigue is common and linked to quality of life and functional status. Fatigue is hypothesized to manifest as multiple types, with general and exertional components. Unique subtypes of fatigue in HF may require differential assessment and treatment to improve outcomes. We conducted this study to identify fatigue subtypes in persons with prevalent HF in the ARIC study (Atherosclerosis Risk in Communities) and describe the distribution of characteristics across subtypes. METHODS: We performed a cross-sectional analysis of 1065 participants with prevalent HF at ARIC visit 5 (2011-2013). We measured exertional fatigue using the Modified Medical Research Council Breathlessness scale and general fatigue using the Patient Reported Outcomes Measurement Information System fatigue scale. We used latent class analysis to identify subtypes of fatigue. Number of classes was determined using model fit statistics, and classes were interpreted and assigned fatigue severity rating based on the conditional probability of endorsing survey items given class. We compared characteristics across classes using multinomial regression. RESULTS: Overall, participants were 54% female and 38% Black with a mean age of 77. We identified 4 latent classes (fatigue subtypes): (1) high general/high exertional fatigue (18%), (2) high general/low exertional fatigue (27%), (3) moderate general/moderate exertional fatigue (20%), and (4) low/no general and exertional fatigue (35%). Female sex, Black race, lower education level, higher body mass index, increased depressive symptoms, and higher prevalence of diabetes were associated with higher levels of general and exertional fatigue. CONCLUSIONS: We identified unique subtypes of fatigue in patients with HF who have not been previously described. Within subtype, general and exertional fatigue were mostly concordant in severity, and exertional fatigue only occurred in conjunction with general fatigue, not alone. Further understanding these fatigue types and their relationships to outcomes may enhance our understanding of the symptom experience and inform prognostication and secondary prevention efforts for persons with HF.

Mixed-methods research to support the use of new lymphoma-specific patient-reported symptom measures derived from the EORTC item library.

BACKGROUND: No specific measures exist to assess patient-reported symptoms experienced by individuals with chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) or mantle cell lymphoma (MCL). This study was conducted to elicit patient-reported CLL/SLL- and MCL-related symptoms and their impact on patients' lives. The study qualitatively and quantitatively evaluated sets of conceptually-selected EORTC Item Library items for assessing CLL/SLL- and MCL-related symptoms. METHODS: The qualitative component of the research included a literature review, clinician consultations, and patient interviews. Concepts important to patients were identified and coded; cognitive debriefing of the selected library items was completed with patients. CLL/SLL and MCL-related symptoms and impacts were organized in a structured conceptual model, which was mapped to item sets from the Item Library. The quantitative component comprised exploratory macro-level Rasch measurement theory (RMT) analysis conducted to provide supportive quantitative insight on the item sets. RESULTS: 41 patients (21-MCL; 20-CLL/SLL) and 5 clinicians participated in the qualitative study; 57 unique patients (30-MCL; 27-CLL/SLL) completed the EORTC items. The conceptual models generated from the qualitative work included symptoms and functional impacts of CLL/SLL and MCL. Symptom domains included swollen lymph nodes, B symptoms, abdominal issues, pain, fatigue, subjective cognitive impairment, anemia-related symptoms, bleeding, infection, and other issues (appetite loss, temperature fluctuation, rash, weight gain, sleep problems, cough). Impacts included physical function, role function, and other functions (psychological, social). Cognitive debriefing demonstrated that the separate item sets for CLL/SLL and MCL-related symptoms were well understood and aligned with patients' experiences. All selected items were included in the conceptual models. The exploratory RMT analysis showed that the item sets provided adequate coverage of the continuum of CLL/SLL- and MCL-related symptom severity. CONCLUSIONS: This study gathered qualitative and early quantitative evidence supporting use of the EORTC Item Library to assess CLL/SLL- and MCL-related symptoms and impacts. These items are promising candidates for measurement of patient-reported disease symptoms in these populations. A larger sample size will be essential to establish the psychometric properties necessary to support use in clinical trials. Patients who suffer from rare cancers of the blood, bone marrow, and lymph nodes can experience chronic and debilitating symptoms. At present, however, there are no dedicated instruments for assessing the patient's experience of symptoms of conditions like chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) or mantle cell lymphoma (MCL), or for assessing their impact on patients' lives. This research project aimed to address that need. The researchers selected relevant and clinically meaningful symptoms from the EORTC Item Library that assess fatigue, B symptoms, and CLL/SLL- and MCL-specific symptoms. Using patients and clinician interviews as well as quantitative analyses, the research revealed no major concerns with using these item sets to assess symptoms of CLL/SLL and MCL. Interviews with patients demonstrated that the separate item sets for CLL/SLL and MCL-related symptoms were well understood and aligned with patients' experiences. All selected items were included in the conceptual models. Item sets identified in this study can potentially be used to assess patient-reported symptom endpoints in clinical trial settings in these disease areas.