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

Heterogeneity in Measures of Illness among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Is Not Explained by Clinical Practice: A Study in Seven U.S. Specialty Clinics

Background: One of the goals of the Multi-site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM) study was to evaluate whether clinicians experienced in diagnosing and caring for patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) recognized the same clinical entity. Methods: We enrolled participants from seven specialty clinics in the United States. We used baseline data (n = 465) on standardized questions measuring general clinical characteristics, functional impairment, post-exertional malaise, fatigue, sleep, neurocognitive/autonomic symptoms, pain, and other symptoms to evaluate whether patient characteristics differed by clinic. Results: We found few statistically significant and no clinically significant differences between clinics in their patients' standardized measures of ME/CFS symptoms and function. Strikingly, patients in each clinic sample and overall showed a wide distribution in all scores and measures. Conclusions: Illness heterogeneity may be an inherent feature of ME/CFS. Presenting research data in scatter plots or histograms will help clarify the challenge. Relying on case-control study designs without subgrouping or stratification of ME/CFS illness characteristics may limit the reproducibility of research findings and could obscure underlying mechanisms.

Activity measurement in pediatric chronic fatigue syndrome

OBJECTIVES

Individuals with myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS) experience debilitating symptoms, including post-exertional malaise, an intensification of symptoms after physical or cognitive exertion. Previous studies found differences in the activity levels and patterns of activity among individuals with ME and CFS, compared to healthy controls; however, limited research exists on the activity levels of pediatric patients. The objective of this study was to examine differences in activity between healthy children and youth with ME and CFS.

METHODS

The present study examines the objective (i.e., ActiGraphy) and self-reported levels of activity among children (ages 5 to 17) enrolled in a community-based study of pediatric CFS.

RESULTS

Children with ME and CFS evidenced lower activity levels than healthy control children. Moreover, participants with ME and CFS evidenced increased nighttime activity and delayed initiation of daytime activity. Participants' self-reported activity data significantly correlated with their ActiGraph data, suggesting that children with ME and CFS are able to accurately describe their activity level.

DISCUSSION

This study highlights differences in activity level and diurnal/nocturnal activity patterns between healthy children and those with ME and CFS. These differences should be considered in identifying appropriate supports and accommodations for children with ME and CFS.

Differences in fatigue-like behavior in the lipopolysaccharide and poly I:C inflammatory animal models

Central fatigue is a condition associated with impairment of the central nervous system often leading to the manifestation of a range of debilitating symptoms. Fatigue can be a consequence of systemic inflammation following an infection. Administration of lipopolysaccharide (LPS) and polyriboinosinic:polyribocytidlic (poly I:C) to animals can induce systemic inflammation by mimicking a bacterial or viral infection respectively and therefore have been used as models of fatigue. We evaluated a range of phenotypic behaviors exhibited in the LPS and poly I:C animal models to assess whether they adequately replicate fatigue symptomology in humans. In addition to standard observation- and intervention-based behavioral assessments, we used powerful in-cage monitoring technology to quantify rodent behavior without external interference. LPS and poly I:C treated Sprague Dawley rats displayed 'sickness behaviors' of elevated temperature, weight loss and reduced activity in the open field test and with in-cage monitoring within 24 h post-treatment, but only LPS-treated rats displayed these behaviors beyond these acute timepoints. Once sickness behavior diminished, LPS-treated rats exhibited an increase in reward-seeking and motivation behaviors. Overall, these results suggest that the LPS animal model produces an extensive and sustained fatigue-like phenotype, whereas the poly I:C model only produced acute effects. Our results suggest that the LPS animal model is a more suitable candidate for further studies on central fatigue-like behavior.

Extracting medication information from unstructured public health data: a demonstration on data from population-based and tertiary-based samples

Background

Unstructured data from clinical epidemiological studies can be valuable and easy to obtain. However, it requires further extraction and processing for data analysis. Doing this manually is labor-intensive, slow and subject to error. In this study, we propose an automation framework for extracting and processing unstructured data.

Methods

The proposed automation framework consisted of two natural language processing (NLP) based tools for unstructured text data for medications and reasons for medication use. We first checked spelling using a spell-check program trained on publicly available knowledge sources and then applied NLP techniques. We mapped medication names into generic names using vocabulary from publicly available knowledge sources. We used WHO’s Anatomical Therapeutic Chemical (ATC) classification system to map generic medication names to medication classes. We processed the reasons for medication with the Lancaster stemmer method and then grouped and mapped to disease classes based on organ systems. Finally, we demonstrated this automation framework on two data sources for Mylagic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS): tertiary-based (n = 378) and population-based (n = 664) samples.

Results

A total of 8681 raw medication records were used for this demonstration. The 1266 distinct medication names (omitting supplements) were condensed to 89 ATC classification system categories. The 1432 distinct raw reasons for medication use were condensed to 65 categories via NLP. Compared to completion of the entire process manually, our automation process reduced the number of the terms requiring manual labor for mapping by 84.4% for medications and 59.4% for reasons for medication use. Additionally, this process improved the precision of the mapped results.

Conclusions

Our automation framework demonstrates the usefulness of NLP strategies even when there is no established mapping database. For a less established database (e.g., reasons for medication use), the method is easily modifiable as new knowledge sources for mapping are introduced. The capability to condense large features into interpretable ones will be valuable for subsequent analytical studies involving techniques such as machine learning and data mining.

Review of case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

BACKGROUND

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown causes. From the perspectives on the etiology and pathophysiology, ME/CFS has been labeled differently, which influenced changes in case definitions and terminologies. This review sought to feature aspects of the history, developments, and differential symptoms in the case definitions.

METHODS

A search was conducted through PubMed published to February 2020 using the following search keywords: case definition AND chronic fatigue syndrome [MeSH Terms]. All reference lists of the included studies were checked. Of the included studies, the number of citations and the visibility in the literatures of the definitions were considered for comparisons of the criteria.

RESULTS

Since the first 'ME' case definition was developed in 1986, 25 case definitions/diagnostic criteria were created based on three conceptual factors (etiology, pathophysiology, and exclusionary disorders). These factors can be categorized into four categories (ME, ME/CFS, CFS, and SEID) and broadly characterized according to primary disorder (ME-viral, CFS-unknown, ME/CFS-inflammatory, SEID-multisystemic), compulsory symptoms (ME and ME/CFS-neuroinflammatory, CFS and SEID-fatigue and/or malaise), and required conditions (ME-infective agent, ME/CFS, CFS, SEID-symptoms associated with fatigue, e.g., duration of illness). ME and ME/CFS widely cover all symptom categories, while CFS mainly covers neurologic and neurocognitive symptoms. Fatigue, cognitive impairment, PEM, sleep disorder, and orthostatic intolerance were the overlapping symptoms of the 4 categories, which were included as SEID criteria.

CONCLUSIONS

This study comprehensively described the journey of the development of case definitions and compared the symptom criteria. This review provides broader insights and explanations to understand the complexity of ME/CFS for clinicians and researchers.

Multi-Site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM): Design and Implementation of a Prospective/Retrospective Rolling Cohort Study

In the Multi-Site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM), we relied on expert clinician diagnoses to enroll patients from 7 specialty clinics in the United States in order to perform a systematic collection of data on measures of myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS). Healthy persons and those with other illnesses that share some features with ME/CFS were enrolled in comparison groups. The major objectives were to: 1) use standardized questionnaires to measure illness domains of ME/CFS and to evaluate patient heterogeneity overall and between clinics; 2) describe the course of illness, identify the measures that best correlate with meaningful clinical differences, and assess the performances of questionnaires as patient/person-reported outcome measures; 3) describe prescribed medications, orders for laboratory and other tests, and management tools used by expert clinicians to care for persons with ME/CFS; 4) collect biospecimens for future hypothesis testing and for evaluation of morning cortisol profiles; and 5) identify measures that best distinguish persons with ME/CFS from those in the comparison groups and detect subgroups of persons with ME/CFS who may have different underlying causes. Enrollment began in 2012 and is planned to continue in multiple stages through 2017. We present the MCAM methods in detail, along with an initial description of the 471 patients with ME/CFS who were enrolled in stage 1.