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

Chronic fatigue syndrome (CFS), sometimes referred to as Myalgic Encephalomyelitis (ME), has frequently been defined by the Fukuda et al. (1994) criteria, which state that a person needs to experience six or more months of chronic fatigue of a new or definite onset, that is not substantially alleviated by rest, not the result of ongoing exertion, and results in substantial reductions in occupational, social, and personal activities. The Fukuda et al. case definition uses polythetic criteria: a set of symptoms in which not all need to be present to make a diagnosis. For example, because the Fukuda et al. criteria only requires four symptoms out of a possible eight, critical CFS symptoms such as post-exertional malaise, and memory and concentration problems are not required of all patients. This case definition for CFS (Fukuda et al., 1994) was characterized by vaguely worded criteria that are lacking operational definitions and guidelines to assist health care professionals in their interpretation and application of the diagnostic tool (Jason, King, et al., 1999; Reeves et al., 2003). In order to provide more guidelines and specific criteria for this case definition, the Centers for Disease Control and Prevention (CDC) developed an empiric case definition for CFS that involved assessment of symptoms, disability, and fatigue using standardized scales (Reeves et al., 2005). Jason, Evans, et al. (in press) used Receiver Operating Characteristics to examine the use of the Multidimensional Fatigue Inventory (MFI) fatigue scale (Smets, Garssen, Bonke, & DeHaes, 1995), which was used to identify whether participants meet the fatigue criterion for the CFS empiric case definition. Jason, Evans, et al. found that the MFI did identify all CFS cases, but these scales were not able to successfully identify those who did not have CFS. In addition, Jason, Brown, et al. (in press) examined Reeves et al.’s recommended use of selected subscales from the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) to measure disability. Notably, Jason, Brown, et al. found that the recommended cutoff of less than or equal to 66.7 on the SF-36 Role-Emotional subscale would select the majority of those with chronic fatigue explained by psychiatric reasons as meeting the CFS disability criterion. Furthermore, the area under the curve (AUC) for the Role-Emotional subscale was the worst among the eight SF-36 subscales for discriminating patients with CFS from controls (Jason, Brown, et al., in press). Using the CFS empiric criteria, the estimated rates of CFS has increased to 2.54% (Reeves et al., 2007), which is ten times higher than prior CDC (Reyes et al., 2003) and other investigator prevalence estimates (Jason, Richman, et al., 1999). Jason et al. (2009) also found that 38% of those with a diagnosis of a Major Depressive Disorder were misclassified as having CFS using the new, more broadly based CDC empiric case definition. To further highlight the challenge of identifying patients with this poorly understood illness, a third case definition was developed for use by clinicians treating patients with ME/CFS, which is referred to as the Canadian clinical case definition for ME/CFS (Carruthers et al., 2003). In contrast to the polythetic method of the Fukuda et al. (1994) criteria, the Canadian clinical case definition requires the presence of symptoms often considered to be hallmarks of this illness such as post-exertional malaise, neurocognitive symptoms, and unrefreshing sleep. Some research suggests the Canadian criteria select a more specific group of patients with this illness. For example, Jason, Torres-Harding, Jurgens, and Helgerson (2004) compared persons meeting the Canadian clinical case definition, the Fukuda et al. criteria, and people experiencing chronic fatigue explained by psychiatric reasons. The Canadian criteria in contrast to the Fukuda et al. criteria selected cases with less psychiatric co-morbidity, more physical functional impairment, and more fatigue/weakness, neuropsychiatric, and neurology symptoms. The differences in symptomatology and prevalence rates across various case definitions of ME/CFS suggest that additional research is needed to determine a diagnostic approach with optimal sensitivity and specificity. Data mining, often also referred to as machine learning, might help determine the types of symptoms that may be most useful in accurately diagnosing chronic fatigue syndrome. In particular, data mining is a technique to explore large sets of data and either 1) replicate human decisions, especially when the process by which these decisions are made are not well-understood or 2) uncover patterns in the data that would not be evident to humans because of the size and complexity of the data. In the particular case of diagnosing CFS, both goals are desirable; using machine learning to augment physicians’ diagnoses could result in more uniform diagnoses, while understanding what symptoms are important in the diagnosis process could allow researchers to focus attention on the evaluation of those symptoms. This study 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. Symptoms with high entropy are deemed important and used to split all the cases into two parts. Successive analysis of symptoms contributing less entropy leads to further branching of the tree, until such branchings produce groupings with homogenous labels. In particular, this study used two sets of symptoms for analysis. One was a subset of the larger set of symptoms that focused on the empirical definition of CFS, while the other was a subset that focused on the Canadian definition. In both cases, the trees attempted to predict the medical diagnosis based on the Fukuda criteria. While the ultimate ability of the tree to predict diagnoses is important, the choice of which symptoms are deemed important, and the values of the symptoms upon which the split is determined also provide useful insight: if the decision tree determines that a symptom is important in the classification, then this symptom can be considered an important contributor to CFS. In addition, the sequence of the symptoms chosen for splitting indicates their relative importance to diagnosis. The study attempted to use a series of questions to differentiate those with CFS from Idiopathic Chronic Fatigue, chronic fatigue explained by psychiatric or medical reasons, and controls. The empirical CFS (Reeves et al.) and Canadian ME/CFS case definitions (Carruthers et al., 2003) were contrasted.