The Fibromyalgia Diagnostic Screen was developed for use by primary care clinicians to assist in the diagnostic evaluation of fibromyalgia, a disorder that predominantly affects women.
The screen was designed to have a patient-completed questionnaire and a clinician-completed section, which included a brief physical examination pertinent to the differential diagnosis of fibromyalgia. The items in the questionnaire were based on patient focus groups and clinician and patient Delphi exercises, which resulted in a ranking of the most common and troublesome fibromyalgia symptoms. One hundred new chronic pain patients (pain > 30 days) and their primary care physicians completed the screen. The patients were grouped as fibromyalgia or nonfibromyalgia by an independent fibromyalgia specialist, who was blind to screen responses. Logistic regression was used to model the probability of fibromyalgia as a function of physician-reported and patient-reported variables. Best subset regression was used to identify a subset of symptoms that were summed to form a single measure. Receiver operating characteristic (ROC) analysis was then used to select thresholds for continuous variables. The symptom and clinical variables were combined to create candidate prediction rules that were compared in terms of sensitivity and specificity to select the best criterion.
Two final models were selected based on overall accuracy in predicting fibromyalgia: one used the patient-reported questionnaire only, and the other added a subset of the physical examination items to this patient questionnaire.
A patient-reported questionnaire with or without a brief physical examination may improve identification of fibromyalgia patients in primary care settings.