CENTER FOR COMMUNITY RESEARCH Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Leonard A. Jason Center for Community Research DePaul University Presentation to the Institute of Medicine’s (IOM), May 5, 2014 CENTER FOR COMMUNITY RESEARCH What is the Natural History of ME/CFS? What Are the Limits of Such Studies Presently? • Very few studies in this area, particularly with best methodology – prospective community-based samples • Jason, Porter et al. (2011a, 2011b) examined the course of ME/CFS over a ten year period of time for a prospective, random, communitybased, multi-ethnic sample – There was relative stability over time on critical measures of disability, fatigue, support, optimism and coping over time – The rate of ME/CFS remained approximately the same over the ten year period of time – Post-exertional malaise best differentiated the ME/CFS group from the other groups (control, Idiopathic chronic fatigue, Medical/Psychiatric reasons for fatigue) • This reaffirms the importance of this being a cardinal and critical symptom for ME/CFS. – For unrefreshing sleep and impaired memory and concentration, 100% of the ME/CFS group had these symptoms • Similar to post-exertional malaise, these results support the idea that unrefreshing sleep and impaired memory and concentration are core symptoms of ME/CFS CENTER FOR COMMUNITY RESEARCH I was asked: Do Certain Symptoms in ME/CFS Appear to Cluster Together? More Precisely: Are Data Available on What Symptoms Covary and How • In order to accurately diagnose an illness or disease, it is important to have a reliable set of criteria for clinicians • Otherwise, it is possible that disagreements about diagnostic decisions may arise because of diagnostic unreliability CENTER FOR COMMUNITY RESEARCH Criterion Variance: Classification of Patients’ Symptoms into Diagnostic Categories • Criterion variance constitutes the largest source of diagnostic unreliability • This typically occurs when an operationally explicit set of criteria is not being utilized in the process of diagnosing an illness • Therefore, a case definition needs to specify the core, cardinal features of ME/CFS – In a recent systematic review, Brurberg, Fønhus, Larun, Flottorp, and Malterud (2014) identified 20 case definitions – Problem is that different case definitions specify different symptoms CENTER FOR COMMUNITY RESEARCH Factor Analysis: Can be used to Determine which Symptoms Covary • Factor analysis identifies latent dimensions • Multiple factor analytic studies of symptomatology have resulted in three to four symptom factors – – – – – Nisenbaum, Reyes, Mawle, & Reeves, 1998 Friedberg, Dechene, McKenzie, & Fontanetta, 2000 Nisenbaum, Reyes, Unger, & Reeves, 2004 Arroll & Senior, 2009 Hickie et al., 2009 CENTER FOR COMMUNITY RESEARCH Brown and Jason’s (2014) Study Identified Three Factors Pain Autonomic Neuroendocrine Immune Fatigue PEM Neurocognitive CENTER FOR COMMUNITY RESEARCH Interpretation of Brown et al. Study • Two of the emergent factors were – Neurological/Cognitive Dysfunction – Post-Exertional Malaise – fit well with previous literature indicating that these are two of the cardinal symptom clusters of ME/CFS • One factor was items from Neuroendocrine, Autonomic, & Immune Dysfunction – more difficult to interpret as it incorporates many symptom clusters • This suggests that there may be core, well-defined symptom clusters such as cognitive impairment and post-exertional malaise – but also that there may be many other symptoms that are experienced differently by patients CENTER FOR COMMUNITY RESEARCH Are There Any Short Screen Tools That Have Been Validated for ME/CFS? • Regardless of which case definition is used – it is critical to assess symptoms in a standardized way to reduce reliability issues • such as the Wagner’s CFS Symptom Inventory • By using a consistent set of items on a questionnaire or measure, as well as cut off points for defining whether a threshold has been met for symptom criteria – clinicians will be able to examine the same illness constructs among all their participants or patients CENTER FOR COMMUNITY RESEARCH Not Easy Determine Whether a Patient Meets a ME/CFS Case Definition • Some investigators have found that over 90% of those with CFS Fukuda also meet the ME/CFS Canadian Clinical criteria (Fluge et al., 2011) whereas others have found the rates closer to 50% (Pheby et al. 2011) – This variability suggests that different investigators might be using different scoring rules for diagnosing ME/CFS using the Canadian Clinical criteria CENTER FOR COMMUNITY RESEARCH DePaul Symptom Questionnaire (DSQ) (Jason et al., 2010) • Developed to provide a structured approach to gathering standardized information and to allow investigators to determine whether or not a patient meets the diagnostic criteria • After completing the DSQ, algorithm determines if a patient meets case definitions including: – Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS; Carruthers et al., 2003) – Myalgic Encephalomyelitis (ME-ICC; Carruthers et al., 2011 – Chronic fatigue syndrome (CFS; Fukuda et al., 1994) CENTER FOR COMMUNITY RESEARCH Psychometric Properties of the DSQ • Good to excellent test-retest reliability (correlation coefficients for items on the DSQ) – Suggests that the overall instrument is a reliable measure for examining symptoms and illness constructs within the patient community • Brown & Jason (2014) indicates excellent internal consistency reliability CENTER FOR COMMUNITY RESEARCH Dissemination • The DSQ is now being used in countries around the world, including Canada, Mexico, Iran, England, Norway, and France • Being used data collection efforts with the CFIDS Association Biobank, CDC multi-site study, Chronic Fatigue Initiative • It is also being used in efforts to document specific vision-related abnormalities among patients – (Hutchinson, Maltby, Badham, & Jason, in press) • A group of Iranian investigators are currently examining other psychometric properties of this instrument. • Specialty Clinic in Vancouver using DSQ all new patients CENTER FOR COMMUNITY RESEARCH Need Standardized Use of Measures • Allow for a well-defined characterization of a patient’s illness • Thus, clinicians will be able to better determine when examining those with ME, ME/CFS, and/or CFS • Ultimately identify and work with more homogenous samples CENTER FOR COMMUNITY RESEARCH DSQ Freely Available • The DePaul Symptom Questionnaire is officially in the REDCap Shared Library – https://redcap.vanderbilt.edu/consortium/library/searc h.php • If your institution does not subscribe to REDCap, you can access the DSQ using this link – https://redcap.is.depaul.edu/surveys/?s=tRxytSPVVw CENTER FOR COMMUNITY RESEARCH In Terms of the Validated Questionnaires and Tools Used for the Diagnosis of ME/CFS, How Do Patients with ME/CFS Compare to Other “Sick" Controls? • ME/CFS is an illness as debilitating as Type II diabetes mellitus, congestive heart failure, Multiple Sclerosis, end-stage renal disease – Anderson & Ferrans (1997); Buchwald, Pearlman, Umali, Schmaling, & Katon (1996) CENTER FOR COMMUNITY RESEARCH What Types of “Sick" Controls Have Been Used in the Past in Your or Others' Work? What Work Is In Progress? • One longitudinal study of youth after developing mono, they those who recovered and who did not were followed up at 6, 12, and 24 months. – Jason, Katz et al. (2013) found that days spent in bed since mono, along with autonomic symptoms, were associated with postinfectious ME/CFS at 6 months • Need include physically active and inactive healthy controls – Such studies could help us explore whether deconditioning is associated with ME/CFS and the major outcome measures • Studies of exercise deconditioning using careful case-control structures have not been able to explain ME/CFS on the basis of exercise deconditioning – Bazelmans, Bleijenberg, van der Meer, & Folgering, (2001); Bruno (2004); van der Werf et al. (2000) CENTER FOR COMMUNITY RESEARCH We Can Distinguish Between ME/CFS and Major Depressive Disorder • Using Discriminant Function Analysis – 100% participants were classified correctly as having ME/CFS or Depressive Disorder • Predictors – Percent of time fatigue was reported – post-exertional malaise – unrefreshing sleep – confusion/disorientation – shortness of breath – severity self-reproach (BDI) • Hawk, Jason, Torres-Harding (2006) CENTER FOR COMMUNITY RESEARCH Jason et al. (1997) compared ME/CFS to MS and Lupus • Early version of our scale differentiates patients with ME/CFS from those who are healthy – it is less likely to distinguish ME/CFS from other autoimmune diseases (especially Lupus) • We will soon be recruiting larger samples of controls with MS and Lupus, to see how they differ from those with ME/CFS using the DSQ • We recommend a two-stage research design with – 1) a screening instrument with good sensitivity – 2) medical assessments of ME/CFS positives from stage 1 to deal with the specificity problem CENTER FOR COMMUNITY RESEARCH Extreme Care with Low Prevalence Illnesses • Based on epidemiological evidence, in a sample of 100,000, there would be approximately 420 cases of ME/CFS • According to Bayes' theorem – If a case definition had a 95% rate of sensitivity and 95% specificity • would identify 399 of the 420 ME/CFS cases • Identify 4,979 individuals who did not have ME/CFS but were identified as having it CENTER FOR COMMUNITY RESEARCH Need Define What Counts as a Symptom • Many questionnaires have measured severity but not frequency, and both need to be considered – Some symptoms are very severe, but if they occur rarely, they are less likely to be considered a problem • Also, many investigators consider mild severity as a cut off point, this decision can lead to including too many individuals into the case definition DePaul Symptom Questionnaire: Frequency and Severity scales for each symptom Scale: Frequency: Severity: 0 1 2 3 4 None of the time A little of the time About half of the time Most of the time All of the time Symptom not present Mild Moderate Severe Very severe CENTER FOR COMMUNITY RESEARCH DePaul Symptom Questionnaire: Frequency and Severity scales for each symptom Frequency and Severity Scores of at least 1: Scale: Frequency: Severity: 0 1 2 3 4 None of the time A little of the time About half of the time Most of the time All of the time Symptom not present Mild Moderate Severe Very severe CENTER FOR COMMUNITY RESEARCH Fatigue 100% 100% Percentage of CFS and Controls with Frequency and Severity Scores >=1 (Fukuda Criteria) Unrefreshing Sleep 99% Post-Exertional Malaise 99% 96% Memory & Concentration Problems 98% 95% 95% 97% 95% 95% Muscle Pain 96% 94% 93% 90% 90% 90% Headaches Joint Pain 86% Sore Throat 81% Tender Lymph Nodes 81% 80% 70% 50% 65% 65% 60% 56% 55% 51% 49% 44% 40% 39% 37% 30% 20% 21% 19% 17% 22% 18% 13% 10% 9% 7% 6% 0% CFS CFS Control Control Misclassifications of Fukuda et al. (1994) CFS 33.7% of controls would meet Fukuda symptom requirements when including participants who report frequency and severity scores of 1 or greater CENTER FOR COMMUNITY RESEARCH DePaul Symptom Questionnaire: Frequency and Severity scales for each symptom Frequency and Severity Scores of at least 2: Scale: Frequency: Severity: 0 1 2 3 4 None of the time A little of the time About half of the time Most of the time All of the time Symptom not present Mild Moderate Severe Very severe CENTER FOR COMMUNITY RESEARCH Percentage of of CFS CFS and and Controls Controls with with Frequency Frequency and and Severity Severity Scores Scores >=1 >=2 Percentage (Fukuda Criteria) Criteria) (Fukuda Unrefreshing Fatigue Sleep Fatigue Unrefreshing 100% 99% 100% Sleep 100% 96% 92% Post-Exertional Malaise 99% 96% Memory & Concentration Problems 98% 95% 95% 97% 95% 95% Post-Exertional Malaise 96% 94% 86% 90% Memory & Concentration Problems 85% 83% 83% 80% 73% 69% 86% 68% 65% Headaches 55% Tender Lymph Nodes 50% 56% 51% 49% 81% 64% 65% 55% Tender Lymph Nodes Joint Pain 73% 69% Sore Throat 81% 66% 60% 60% Joint Pain Muscle Pain 80% 80% 70% 70% Headaches 93% 90% 90% 90% 50% 50% Muscle Pain 44% 40% 40% Sore Throat 44% 39% 37% 31% 30% 30% 20% 20% 16% 17% 10% 10% 18% 13% 12% 10% 7% 0% 0% 22% 21% 19% 7% 7% 2% 7% 4% 5% 4% 7% 2% CFS CFS CFS 9% 2% 7% 5% Control Control Control 2% 1% 6% 0% Canadian Clinical ME/CFS (2003) criteria • Six or more months of fatigue • One symptom from each of the following categories: – – – – Post-Exertional Malaise Sleep Dysfunction Neurocognitive Impairments Pain • One symptom from two of the following categories: – Autonomic – Neuroendocrine – Immune CENTER FOR COMMUNITY RESEARCH Percentage of CFS and Controls with Frequency and Severity Scores >=2 (ME/CFS Symptoms) 100% Sleep 90% PEM Neurocognitive 80% Pain Immune, Neuroendocrine, & Autonomic symptoms have lower prevalence 70% 60% Immune Neuroendocrine Autonomic 50% 40% 30% 20% 10% 0% CFS Control CENTER FOR COMMUNITY RESEARCH Are There Any Data on How Patients of Different Ethnic/ Socio-Economic Backgrounds Present? Are There Any Differences in Terms of Their Presentation or Course of Illness? • Few studies have examined these questions • Most research has been on Caucasian samples CENTER FOR COMMUNITY RESEARCH Data from a Community-Based Sample Jason, Taylor, Kennedy et al. (2001) Symptoms experienced more severely by Minority participants 100 90 80 70 60 Severity Rating Minority Caucasian 50 40 30 20 10 0 Sore throat Postexertional Malaise Headaches Symptom Unrefreshing Sleep CENTER FOR COMMUNITY RESEARCH Song, Jason, & Taylor(1999) Examined Sociodemographic Differences • Examined fatigue across African American, Caucasian, Latino and Asian American samples • Latinos who were female, older, and of lower SES reported the highest relative severity of fatigue CENTER FOR COMMUNITY RESEARCH Women Latinas Highest Fatigue Female 13.44 13.50 Male 13.10 13.00 Mean Fatigue 13.00 12.50 12.90 12.49 12.38 12.29 12.00 12.00 11.50 11.00 African Americans Caucasians Latinos Racial/ethnic Groups Asian Americans CENTER FOR COMMUNITY RESEARCH Among Latinos, Highest Fatigue Found Among Those Older and Lower SES Latinos Younger 50% 13.4 Older 50% 13.22 13.2 13 12.91 12.80 Mean Fatigue 12.8 12.6 12.4 12.23 12.2 12 11.8 11.6 Lower 50% Upper 50% SES CENTER FOR COMMUNITY RESEARCH In What Ways Are Community-Based Patients Different from Those Recruited From Referral/ Specialty Centers? • Issue not been well explored • Almost all studies of samples with patients with ME/CFS have relied on referrals from physicians or health facilities CENTER FOR COMMUNITY RESEARCH Jason, Plioplys et al. (2003) Compared Individuals Diagnosed with ME/CFS in a Community-Based Sample to Patients with ME/CFS Who Were Recruited From Tertiary-Care • Significantly more minorities in the Community versus Tertiary samples • Within the ME/CFS-Community sample, 45% were white, 16% were Black, 29% were Latino, and 10% were other • In the ME/CFS-Tertiary sample 93% were white, 5% were Black, 1% were Latino and 1% were other • However, symptom criteria were significantly higher among Tertiary as compared with the Community samples – memory and concentration problems, 96% vs 74% – sore throat, 76% vs 45% – tender lymph nodes, 65% vs 45% CENTER FOR COMMUNITY RESEARCH Thoughts on the recent IOM with Gulf War Veterans • In their review of factor-analytic studies, key question is whether the factor structure varies among compared populations • As the report stated, that question is most appropriately posited as a formal statistical test – the probability of observing the differences between the factor structures in the samples is estimated under the null hypothesis that the factor structures are the same in the two populations • Unfortunately, almost all existing studies of factorstructure differences have failed to test the hypothesis directly – none have used structural equation models CENTER FOR COMMUNITY RESEARCH Issues Needing Resolution • Reduce criterion variance by deciding which case definition to use – Facilitate clinicians identify patients similar core symptoms • Specify what instrument to use to measure the symptoms – Develop algorithms to help determine whether a patient meets the case definition • Encourage research on ways to better operationalize key elements of the case definition – – – – Define Onset Define Substantial Reductions Define Lifelong fatigue Define Time Period for Symptoms (6 months, 1 month, 1 week, today) – Define Cutoffs for Frequency and Severity of Symptoms 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 1979 1978 1977 1976 1975 1974 Illness Timeline: Level of Functioning over Time 100 90 80 70 60 50 40 30 Health significantly deteriorating 20 Chronic Bronchitis 10 0 CENTER FOR COMMUNITY RESEARCH Future Directions • In the critical decisions before your committee – Collect and share data from patient groups, clinicians, NIH, CDC, IACFS/ME using quantitative and qualitative methods to inform an interactive and transparent process – This will help secure the participation of key stakeholders • Learn from experiences of other diseases which developed infrastructures to oversee refinements of case definition criteria – Recommend the development of an ongoing, flexible, adaptive system that encourages clinical trials, research, incorporation of new findings into the case definition CENTER FOR COMMUNITY RESEARCH References • • • • • • • • • • • • • • • • Anderson, J. S., & Ferrans, C. E. (1997). The quality of life of persons with chronic fatigue syndrome. Journal of Nervous Mental Disorders, 185, 359-367. Brown, A., & Jason, L.A. (2014). Validating a measure of myalgic encephalomyelitis/chronic fatigue syndrome symptomatology.. Manuscript under review. Brurberg, K.G., Fønhus, M.S., Larun, L., Flottorp, S., & Malterud, K. (2014). (CFS/ME): a systematic review syndrome/myalgic encephalomyelitis BMJ Open 2014 4:e003973 Buchwald, D., Pearlman, T., Umali, J., Schmaling, K., & Katon, W. (1996). Functional status in patients with chronic fatigue syndrome, other fatiguing illnesses, and healthy individuals. American Journal of Medicine, 101, 364–370. Hawk, C., Jason, L.A., & Torres-Harding, S. (2006). Differential diagnosis of chronic fatigue syndrome and major depressive disorder. International Journal of Behavioral Medicine, 13, 244-251. Hutchinson, C.V., Maltby, J., Badham, S.P., & Jason, L.A. (in press). Vision-related symptoms as a clinical feature of Chronic Fatigue Syndrome/Myalgic Encephalomyelitis? Evidence from the DePaul Symptom Questionnaire. British Journal of Ophthalmology. doi:10.1136/bjophthalmol-2013-304439 Jason, L. A., Brown, A., Evans, M., Sunnquist, M., & Newton, J. L. (2013). Contrasting chronic fatigue syndrome versus myalgic encephalomyelitis/chronic fatigue syndrome. Fatigue: Biomedicine, Health & Behavior, 1(3), 168-183. Jason, L.A., Katz, B.Z., Shiraishi, Y., Mears, C.J., Im, Y., Taylor, R.R. (2014). Predictors of post-infectious chronic fatigue syndrome in adolescents. Health Psychology and Behavioral Medicine: An Open Access Journal, 2, 41–51. Jason, L.A., Plioplys, A.V., Torres-Harding, S., & Corradi, K. (2003). Comparing symptoms of chronic fatigue syndrome in a community-based versus tertiary care sample. Journal of Health Psychology, 8, 459-464. Jason, L.A., Porter, N., Hunnell, J., Brown, A., Rademaker, A., & Richman, J.A. (2011a). A natural history study of chronic fatigue syndrome. Rehabilitation Psychology, 56, 32-42. PMCID: PMC3171164 Jason, L.A., Porter, N., Hunnell, J., Rademaker, A., & Richman, J.A. (2011b). CFS prevalence and risk factors over time. Journal of Health Psychology, 16, 445-456. PMCID: PMC3166209 Jason, L.A., Ropacki, M.T., Santoro, N.B., Richman, J.A., Heatherly, W., Taylor, R.R., Ferrari, J.R., Haney-Davis, T.M., Rademaker, A., Dupuis, J., Golding, J., Plioplys, A.V., & Plioplys, S. (1997). A screening instrument for Chronic Fatigue Syndrome: Reliability and validity. Journal of Chronic Fatigue Syndrome, 3, 39-59. Jason, L.A., Taylor, R.R., Kennedy, C.L., Harding, S.T., Song, S., Johnson, D., Chimata, R. (2001). Subtypes of chronic fatigue syndrome: A review of findings. Journal of Chronic Fatigue Syndrome, 8, 1-21 Jason, L.A., Taylor, R.R., Kennedy, C.L., Jordan, K., Song, S., Johnson, D., & Torres, S. (2000). Chronic fatigue syndrome: Sociodemographic subtypes in a community-based sample. Evaluation and the Health Professions, 23, 243-263. Song, S., Jason, L.A., Taylor, R.R., Torres-Harding, S.R., Helgerson, J., & Witter, E. (2002). Fatigue severity among African Americans: Gender and age interactions. Journal of Black Psychology, 28, 53-65. Song, S., Jason, L.A., & Taylor, R.R. (1999). The relationship between ethnicity and fatigue in a community-based sample. Journal of Gender, Culture, and Health, 4, 255-268.