Purpose: To determine the diagnostic accuracy for single symptoms and clusters of symptoms to distinguish between individuals with and without chronic fatigue syndrome (CFS).
Methods: A cohort study was conducted in an exercise physiology laboratory in an academic setting. Thirty subjects participated in this study (n = 16 individuals with CFS; n = 14 non-disabled sedentary matched control subjects). An open-ended symptom questionnaire was administered 1 week following the second of two maximal cardiopulmonary exercise tests administered 24 h apart.
Receiver operating characteristics (ROC) curve analysis was significant for failure to recover within 1 day (area under the curve = 0.864, 95% confidence interval [CI]: 0.706–1.00, p = 0.001) but not within 7 days.
Clinimetric properties of failure to recover within 1 day to predict membership in the CFS cohort were sensitivity 0.80, specificity 0.93, positive predictive value 0.92, negative predictive value 0.81, positive likelihood ratio 11.4, and negative likelihood ratio 0.22.
Fatigue demonstrated high sensitivity and modest specificity to distinguish between cohorts, while neuroendocrine dysfunction, immune dysfunction, pain, and sleep disturbance demonstrated high specificity and modest sensitivity.
ROC analysis suggested cut-point of two associated symptoms (0.871, 95% CI: 0.717–1.00, p < 0.001). A significant binary logistic regression model (p < 0.001) revealed immune abnormalities, sleep disturbance and pain accurately classified 92% of individuals with CFS and 88% of control subjects.
Conclusions: A cluster of associated symptoms distinguishes between individuals with and without CFS. Fewer associated symptoms may be necessary to establish a diagnosis of CFS than currently described.
Source: Disability and Rehabilitation, Jan 6, 2011. PMID: 21208154, by Davenport TE, Stevens SR, Baroni K, Van Ness M, Snell CR. Department of Physical Therapy, Thomas J. Long School of Pharmacy and Health Sciences, and Pacific Fatigue Laboratory, Department of Sport Sciences, University of the Pacific, Stockton, California, USA. [Email: email@example.com]