Methodology – Page 8 – American ME and CFS Society

Assessing current functioning as a measure of significant reduction in activity level

Abstract:

BACKGROUND: Myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) have case definitions with varying criteria, but almost all criteria require an individual to have a substantial reduction in activity level. Unfortunately, a consensus has not been reached regarding what constitutes substantial reductions. One measure that has been used to measure substantial reduction is the Medical Outcomes Study Short Form-36 Health Survey (SF-36).[1].

PURPOSE: The current study examined the relationship between the SF-36, a measure of current functioning, and a self-report measure of the percent reduction in hours spent on activities.

RESULTS: Findings indicated that select subscales of the SF-36 accurately measure significant reductions in functioning. Further, this measure significantly differentiates patients from controls.

CONCLUSION: Determining what constitutes a significant reduction in activity is difficult because it is subjective to the individual. However, certain subscales of the SF-36 could provide a uniform way to accurately measure and define substantial reductions in functioning.

Source: Thorpe T, McManimen S, Gleason K, Stoothoff J, Newton JL, Strand EB, Jason LA. Assessing current functioning as a measure of significant reduction in activity level. Fatigue. 2016;4(3):175-188. doi: 10.1080/21641846.2016.1206176. Epub 2016 Jul 19. https://www.ncbi.nlm.nih.gov/pubmed/28217427

Validation of the Flinders Fatigue Scale as a measure of daytime fatigue

Abstract:

STUDY OBJECTIVES: To clinically validate the Flinders Fatigue Scale (FFS) as a brief measure of daytime fatigue, and to derive cut-off scores to classify fatigue severity.

METHOD: The FFS was administered to 439 adult volunteers from the general population, 292 adults with insomnia, 132 adults with Obstructive Sleep Apnoea (OSA) and 66 adults with Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), together with the Fatigue Severity Scale (FSS) and the Epworth Sleepiness Scale (ESS).

RESULTS: A factor analysis revealed a single factor solution for the seven-item scale (67% of total variance), although a better fit was obtained for a modified six-item version (75% of total variance). Group FFS scores varied in accordance with theorised fatigue levels, with CFS/ME and insomnia samples reporting significantly higher fatigue than OSA and volunteer samples. Good convergent validity was established with the FSS for volunteer (r = 0.67) and CFS/ME samples (r = 0.61). Excellent discriminant validity with the ESS was observed for the insomnia (r = -0.08) and CFS/ME groups (r = 0.03), while a small-to-moderate correlation was found within the volunteer sample (r = 0.29). Cut-off scores were identified to categorise borderline (13-15), moderate (16-20) and severe (≥21) fatigue.

CONCLUSIONS: The FFS is a reliable and valid instrument to quantify subjective daytime fatigue. Sensitivity and specificity analyses indicate scores that best discriminate insomniacs and CFS/ME populations from a non-clinical population. However, it is proposed that the data can also be used to indicate the severity of fatigue by reference to these first two groups.

Source: Cameron K, Williamson P, Short MA, Gradisar M. Validation of the Flinders Fatigue Scale as a measure of daytime fatigue. Sleep Med. 2017 Feb;30:105-112. doi: 10.1016/j.sleep.2016.11.016. Epub 2016 Dec 3. https://www.ncbi.nlm.nih.gov/pubmed/28215232

Metagenomic Investigation of Plasma in Individuals with ME/CFS Highlights the Importance of Technical Controls to Elucidate Contamination and Batch Effects

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease causing indefinite fatigue. ME/CFS has long been hypothesised to have an infectious cause; however, no specific infectious agent has been identified.

We used metagenomics to analyse the RNA from plasma samples from 25 individuals with ME/CFS and compare their microbial content to technical controls as well as three control groups: individuals with alternatively diagnosed chronic Lyme syndrome (N = 13), systemic lupus erythematosus (N = 11), and healthy controls (N = 25).

We found that the majority of sequencing reads were removed during host subtraction, thus there was very low microbial RNA content in the plasma. The effects of sample batching and contamination during sample processing proved to outweigh the effects of study group on microbial RNA content, as the few differences in bacterial or viral RNA abundance we did observe between study groups were most likely caused by contamination and batch effects.

Our results highlight the importance of including negative controls in all metagenomic analyses, since there was considerable overlap between bacterial content identified in study samples and control samples. For example, Proteobacteria, Firmicutes, Actinobacteria, and Bacteriodes were found in both study samples and plasma-free negative controls. Many of the taxonomic groups we saw in our plasma-free negative control samples have previously been associated with diseases, including ME/CFS, demonstrating how incorrect conclusions may arise if controls are not used and batch effects not accounted for.

Source: Miller RR, Uyaguari-Diaz M, McCabe MN, Montoya V, Gardy JL, Parker S, Steiner T, Hsiao W, Nesbitt MJ, Tang P, Patrick DM; CCD Study Group. Metagenomic Investigation of Plasma in Individuals with ME/CFS Highlights the Importance of Technical Controls to Elucidate Contamination and Batch Effects. PLoS One. 2016 Nov 2;11(11):e0165691. doi: 10.1371/journal.pone.0165691. ECollection 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5091812/ (Full article)

The utility of patient-reported outcome measures among patients with myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

PURPOSE: Debilitating fatigue is a core symptom of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS); however, the utility of patient-reported symptom outcome measures of fatigue for ME/CFS patients is problematic due to ceiling effects and issues with reliability and validity. We sought to evaluate the performance of three patient-reported symptom measures in a sample of ME/CFS patients and matched controls.

METHODS: Two hundred and forty ME/CFS patients and 88 age, sex, race, and zip code matched controls participated in the study. Participants completed the Multidimensional Fatigue Inventory-20, DePaul Symptom Questionnaire, and RAND SF-36.

RESULTS: The general and physical fatigue subscales on Multidimensional Fatigue Inventory-20, as well as the role of physical health on the RAND SF-36, demonstrated questionable or unacceptable internal consistency and problematic ceiling effects. The DePaul Symptom Questionnaire demonstrated excellent internal reliability, and less than 5 % of participants were at the ceiling on each subscale. The post-exertional malaise subscale on the DePaul Symptom Questionnaire demonstrated excellent clinical utility as it was able to differentiate between ME/CFS patients and controls (OR 1.23, p < .001) and predicted ceiling effects on other patient-reported outcome subscales. A score of 20 on the post-exertional malaise subscale of the DePaul Symptom Questionnaire optimally differentiated between patients and controls.

CONCLUSIONS: Significant ceiling effects and concerns with reliability and validity were observed among Multidimensional Fatigue Inventory-20 and RAND SF-36 subscales for ME/CFS patients. The DePaul Symptom Questionnaire addresses a number of concerns typically identified when using patient-reported outcome measures with ME/CFS patients; however, an improved multidimensional patient-reported outcome tool for measuring ME/CFS-related symptoms is warranted.

Source: Murdock KW, Wang XS, Shi Q, Cleeland CS, Fagundes CP, Vernon SD. The utility of patient-reported outcome measures among patients with myalgic encephalomyelitis/chronic fatigue syndrome. Qual Life Res. 2016 Sep 6. [Epub ahead of print] https://www.ncbi.nlm.nih.gov/pubmed/27600520

Methods of applying the 1994 case definition of chronic fatigue syndrome – impact on classification and observed illness characteristics

Abstract:

BACKGROUND: Multiple case definitions are in use to identify chronic fatigue syndrome (CFS). Even when using the same definition, methods used to apply definitional criteria may affect results. The Centers for Disease Control and Prevention (CDC) conducted two population-based studies estimating CFS prevalence using the 1994 case definition; one relied on direct questions for criteria of fatigue, functional impairment and symptoms (1997 Wichita; Method 1), and the other used subscale score thresholds of standardized questionnaires for criteria (2004 Georgia; Method 2). Compared to previous reports the 2004 CFS prevalence estimate was higher, raising questions about whether changes in the method of operationalizing affected this and illness characteristics.

METHODS: The follow-up of the Georgia cohort allowed direct comparison of both methods of applying the 1994 case definition. Of 1961 participants (53 % of eligible) who completed the detailed telephone interview, 919 (47 %) were eligible for and 751 (81 %) underwent clinical evaluation including medical/psychiatric evaluations. Data from the 499 individuals with complete data and without exclusionary conditions was available for this analysis.

RESULTS: A total of 86 participants were classified as CFS by one or both methods; 44 cases identified by both methods, 15 only identified by Method 1, and 27 only identified by Method 2 (Kappa 0.63; 95 % confidence interval [CI]: 0.53, 0.73 and concordance 91.59 %). The CFS group identified by both methods were more fatigued, had worse functioning, and more symptoms than those identified by only one method. Moderate to severe depression was noted in only one individual who was classified as CFS by both methods. When comparing the CFS groups identified by only one method, those only identified by Method 2 were either similar to or more severely affected in fatigue, function, and symptoms than those only identified by Method 1.

CONCLUSIONS: The two methods demonstrated substantial concordance. While Method 2 classified more participants as CFS, there was no indication that they were less severely ill or more depressed. The classification differences do not fully explain the prevalence increase noted in the 2004 Georgia study. Use of standardized instruments for the major CFS domains provides advantages for disease stratification and comparing CFS patients to other illnesses.

Source: Unger ER, Lin JM, Tian H, Gurbaxani BM, Boneva RS, Jones JF. Methods of applying the 1994 case definition of chronic fatigue syndrome – impact on classification and observed illness characteristics. Popul Health Metr. 2016 Mar 12;14:5. doi: 10.1186/s12963-016-0077-1. eCollection 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788915/ (Full article)

Development of the Sensory Hypersensitivity Scale (SHS): a self-report tool for assessing sensitivity to sensory stimuli

Abstract:

Sensory hypersensitivity is one manifestation of the central sensitization that may underlie conditions such as fibromyalgia and chronic fatigue syndrome. We conducted five studies designed to develop and validate the Sensory Hypersensitive Scale (SHS); a 25-item self-report measure of sensory hypersensitivity.

The SHS assesses both general sensitivity and modality-specific sensitivity (e.g. touch, taste, and hearing). 1202 participants (157 individuals with chronic pain) completed the SHS, which demonstrated an adequate overall internal reliability (Cronbach’s alpha) of 0.81, suggesting the tool can be used as a cross-modality assessment of sensitivity. SHS scores demonstrated only modest correlations (Pearson’s r) with depressive symptoms (0.19) and anxiety (0.28), suggesting a low level of overlap with psychiatric complaints. Overall SHS scores showed significant but relatively modest correlations (Pearson’s r) with three measures of sensory testing: cold pain tolerance (-0.34); heat pain tolerance (-0.285); heat pain threshold (-0.271).

Women reported significantly higher scores on the SHS than did men, although gender-based differences were small. In a chronic pain sample, individuals with fibromyalgia syndrome demonstrated significantly higher SHS scores than did individuals with osteoarthritis or back pain. The SHS appears suitable as a screening measure for sensory hypersensitivity, though additional research is warranted to determine its suitability as a proxy for central sensitization.

Source: Dixon EA, Benham G, Sturgeon JA, Mackey S, Johnson KA, Younger J. Development of the Sensory Hypersensitivity Scale (SHS): a self-report tool for assessing sensitivity to sensory stimuli. J Behav Med. 2016 Jun;39(3):537-50. doi: 10.1007/s10865-016-9720-3. Epub 2016 Feb 12. https://www.ncbi.nlm.nih.gov/pubmed/26873609

Capturing the post-exertional exacerbation of fatigue following physical and cognitive challenge in patients with chronic fatigue syndrome

Abstract:

OBJECTIVE: To design and validate an instrument to capture the characteristic post-exertional exacerbation of fatigue in patients with chronic fatigue syndrome (CFS).

METHODS: Firstly, patients with CFS (N=19) participated in five focus group discussions to jointly explore the nature of fatigue and dynamic changes after activity, and inform development of a self-report instrument – the Fatigue and Energy Scale (FES). The psychometric properties of the FES were then examined in two case-control challenge studies: a physically-demanding challenge (moderate-intensity aerobic exercise; N=10 patients), and a cognitively-demanding challenge (simulated driving; N=11 patients). Finally, ecological validity was evaluated by recording in association with tasks of daily living (N=9).

RESULTS: Common descriptors for fatigue included ‘exhaustion’, ‘tiredness’, ‘drained of energy’, ‘heaviness in the limbs’, and ‘foggy in the head’. Based on the qualitative data, fatigue was conceptualised as consisting of ‘physical’ and ‘cognitive’ dimensions. Analysis of the psychometric properties of the FES showed good sensitivity to the changing symptoms during a post-exertional exacerbation of fatigue following both physical exercise and driving simulation challenges, as well as tasks of daily living.

CONCLUSION: The ‘fatigue’ experienced by patients with CFS covers both physical and cognitive components. The FES captured the phenomenon of a post-exertional exacerbation of fatigue commonly reported by patients with CFS. The characteristics of the symptom response to physical and cognitive challenges were similar. Both the FES and the challenge paradigms offer key tools to reliably investigate biological correlates of the dynamic changes in fatigue.

Source: Keech A, Sandler CX, Vollmer-Conna U, Cvejic E, Lloyd AR, Barry BK. Capturing the post-exertional exacerbation of fatigue following physical and cognitive challenge in patients with chronic fatigue syndrome. J Psychosom Res. 2015 Dec;79(6):537-49. doi: 10.1016/j.jpsychores.2015.08.008. Epub 2015 Sep 2. https://www.ncbi.nlm.nih.gov/pubmed/26359713

Modeling diurnal hormone profiles by hierarchical state space models

Abstract:

Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls.

Source: Liu Z, Guo W. Modeling diurnal hormone profiles by hierarchical state space models. Stat Med. 2015 Oct 30;34(24):3223-34. doi: 10.1002/sim.6579. Epub 2015 Jul 7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4592415/ (Full article)

Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome

Abstract:

BACKGROUND: The current practice of using only a few strongly associated genetic markers in regression models results in generally low power in prediction or accounting for heritability of complex human traits.

PURPOSE: We illustrate here a Bayesian joint estimation of single nucleotide polymorphism (SNP) effects principle to improve prediction of phenotype status from pathway-focused sets of SNPs. Chronic fatigue syndrome (CFS), a complex disease of unknown etiology with no laboratory methods for diagnosis, was chosen to demonstrate the power of this Bayesian method. For CFS, such a genetic predictive model in combination with clinical evidence might lead to an earlier diagnosis than one based solely on clinical findings.

METHODS: One of our goals is to model disease status using Bayesian statistics which perform variable selection and parameter estimation simultaneously and which can induce the sparseness and smoothness of the SNP effects. Smoothness of the SNP effects is obtained by explicit modeling of the covariance structure of the SNP effects.

RESULTS: The Bayesian model achieved perfect goodness of fit when tested within the sampled data. Tenfold cross-validation resulted in 80% accuracy, one of the best so far for CFS in comparison to previous prediction models. Model reduction aspects were investigated in a computationally feasible manner. Additionally, genetic variation estimates provided by the model identified specific genetic markers for their biological role in the disease pathophysiology.

CONCLUSIONS: This proof-of-principle study provides a powerful approach combining Bayesian methods, SNPs representing multiple pathways and rigorous case ascertainment for accurate genetic risk prediction modeling of complex diseases like CFS and other chronic diseases.

Source: Bhattacharjee M, Rajeevan MS, Sillanpää MJ. Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome. Hum Genomics. 2015 Jun 11;9:8. doi: 10.1186/s40246-015-0030-6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4479222/ (Full article)

Measuring substantial reductions in activity

Abstract:

The case definitions for Myalgic Encephalomyelitis/chronic fatigue syndrome (ME/CFS), ME, and CFS each include a disability criterion requiring substantial reductions in activity in order to meet diagnostic criteria. Difficulties have been encountered in defining and operationalizing the substantial reduction disability criterion within these various illness definitions.

The present study sought to relate measures of past and current activities in several domains including the SF-36, an objective measure of activity (e.g., actigraphy), a self-reported quality of life scale, and measures of symptom severity.

Results of the study revealed that current work activities had the highest number of significant associations with domains such as the SF-36 subscales, actigraphy, and symptom scores. As an example, higher self-reported levels of current work activity were associated with better health. This suggests that current work related activities may provide a useful domain for helping operationalize the construct of substantial reductions in activity.

Source: Schafer C, Evans M, Jason LA, So S, Brown A. Measuring substantial reductions in activity. J Prev Interv Community. 2015;43(1):5-19. doi: 10.1080/10852352.2014.973242. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295629/ (Full article)