Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition

Jason, Leonard A.; Skendrovic, Beth; Furst, Jacob; Brown, Abigail; Weng, Angela; Bronikowski, Christine

doi:10.1002/jclp.20827

Cited by 18 publications

(22 citation statements)

References 28 publications

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“…Several studies have analyzed which symptoms best differentiate patients from controls [18]. For example, Jason et al [19] used several scoring methods (i.e., continuous scores of symptoms, theoretically and empirically derived cut off scores of symptoms) in identifying core symptoms that could best separate patients from controls.…”

Section: Introductionmentioning

confidence: 99%

Factor Analysis of the DePaul Symptom Questionnaire: Identifying Core Domains

et al. 2015

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The present study attempted to identify critical symptom domains of individuals with Myalgic Encephalomyelitis (ME) and chronic fatigue syndrome (CFS). Using patient and control samples collected in the United States, Great Britain, and Norway, exploratory factor analysis (EFA) was used to establish the underlying factor structure of ME and CFS symptoms. The EFA suggested a four-factor solution: post-exertional malaise, cognitive dysfunction, sleep difficulties, and a combined factor consisting of neuroendocrine, autonomic, and immune dysfunction symptoms. The use of empirical methods could help better understand the fundamental symptom domains of this illness.

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Section: Introductionmentioning

confidence: 99%

Factor Analysis of the DePaul Symptom Questionnaire: Identifying Core Domains

et al. 2015

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“…More empirical methods have been used to identify symptoms that differentiate CFS samples from controls. [31–32] For example, Jason, Kot, et al [33] used data mining techniques to separate patients with CFS from controls. Outcomes from these analyses suggest that individuals identified using fewer, but empirically selected, symptoms (i.e., fatigue or extreme tiredness, physically drained/sick after mild activity, difficulty finding the right word to say or expressing thoughts, and unrefreshing sleep) could accurately identify patients and controls.…”

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confidence: 99%

Case definitions integrating empiric and consensus perspectives

Jason

McManimen

Sunnquist

et al. 2016

Fatigue: Biomedicine, Health & Behavior

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Background There has been considerable controversy regarding how to name and define the illnesses known as myalgic encephalomyelitis (ME) and chronic fatigue syndrome (CFS). The IOM report has proposed a new clinical criteria and name for this illness, but aspects of these recommendations have been scrutinized by patients and scientists. Purpose It is possible that both empiric and consensus approaches could be used to help settle some of these diagnostic challenges. Using patient samples collected in the United States, Great Britain, and Norway (N=556), the current study attempted to categorize patients using more general as well as more restricted case definitions. Results Overall, the outcomes suggest that there might be four groupings of patients, with the broadest category involving those with chronic fatigue (N=62), defined by 6 or more months of fatigue which can be cannot be explained by medical or psychiatric conditions. A second category involves those patients that have chronic fatigue that can be explained by a medical or psychiatric condition (N=47). A third category involves more specific criteria that have been posited both by the IOM report, a Canadian Clinical Case criteria, a ME-ICC criteria and a more empiric approach. These efforts have specified domains of substantial reductions of activity, post-exertional malaise, neurocognitive impairment, and sleep dysfunction (N=346). Patients with these characteristics were more functionally impaired than those meeting just chronic fatigue criteria, p < .05. Finally, those meeting even more restrictive ME criteria proposed by Ramsay, identified a smaller and even more impaired group, p < .05. Discussion The advantages of using such empirical and consensus approaches to develop reliable classification and diagnostic efforts are discussed.

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“…In particular, data mining can uncover patterns in the data that would not be evident to humans because of the size and complexity of the data. Jason, Skendrovic et al (2012) explored the use of decision trees to implement the data mining. Decision trees attempt to predict a classification (diagnosis) for each patient based on successive binary choices: at each branch point of the tree, all the symptoms are examined with respect to their effect on the entropy of the diagnoses.…”

Section: Case Definitionsmentioning

confidence: 99%

“…If the decision tree determines that a symptom is important in the classification, then this symptom can be considered an important contributor to the illness. Using this approach, Jason, Skendrovic et al (2012) compared the Canadian ME/CFS criteria (Carruthers et al, 2003) to the empirical CFS criteria developed by Reeves et al (2005). The Reeves et al CFS (2005) criteria were able to correctly identify 79% of the CFS cases, whereas the Canadian ME/CFS criteria were able to discriminate 87% of the cases.…”

Section: Case Definitionsmentioning

confidence: 99%

Defining Essential Features of Myalgic Encephalomyelitis and Chronic Fatigue Syndrome

Jason¹,

Sunnquist²,

Brown³

et al. 2015

Journal of Human Behavior in the Social Environment

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Considerable debate surrounds the search for the defining features of patients with Myalgic Encephalomyelitis (ME) and chronic fatigue syndrome (CFS). Current case definitions were created through clinical consensus. Failure to operationalize these case definitions has led to considerable variability in the identification of patients. In addition, some case definitions (e.g., Fukuda et al., 1994) do not require cardinal symptoms of this illness, where as other case definitions do require core symptoms of this illness (Carruthers et al., 2003, 2011), and these latter case criteria appear to identify a more impaired group of patients. Criterion variance is most likely to occur when operationally explicit criteria do not exist for diagnostic categories (Spitzer, Endicott, & Robins, 1978), or when there are varying criteria for contrasting case definitions, which is an impediment to the research in this field. To deal with this problem, it is possible to differentiate those that meet more loosely defined criteria from those that are more narrowly and defined, thus differentiating CFS from ME. In order to progress the search for biological markers and effective treatments, essential features need to be operationalized and broadly used in order to increase the probability that individuals included in samples have the same underlying illness.

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Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition

Cited by 18 publications

References 28 publications

Factor Analysis of the DePaul Symptom Questionnaire: Identifying Core Domains

Factor Analysis of the DePaul Symptom Questionnaire: Identifying Core Domains

Case definitions integrating empiric and consensus perspectives

Defining Essential Features of Myalgic Encephalomyelitis and Chronic Fatigue Syndrome

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