A diagnostic algorithm for distinguishing the eosinophilia-myalgia syndrome from fibromyalgia and chronic myofascial pain.
OBJECTIVE
To develop a diagnostic algorithm for the eosinophilia-myalgia syndrome (EMS) that complements the existing case definition.


METHODS
We conducted a retrospective study using data on 59 clinical and laboratory variables from a consecutive referral cohort of 91 patients with EMS meeting the Centers for Disease Control and Prevention case definition. Age and sex matched controls included 93 patients with fibromyalgia and 99 patients with chronic myofascial pain. The study period was March 1989 to April 1992. Recursive partitioning was used to create a diagnostic algorithm.


RESULTS
In the 283 case patients and controls with disabling myalgias, 4 differentiating variables identified patients with EMS: extremity edema, leukocyte count > 12.5 x 10(9)/l, dyspnea, and absence of arthralgias. These 4 variables form a diagnostic algorithm that has a sensitivity of 95.6%, a specificity of 96.9%, and positive and negative predictive values of 93.5 and 97.9%, respectively.


CONCLUSIONS
This algorithm is practical and can be easily applied in any medical setting. It also readily distinguishes EMS from other common myalgia syndromes.
Department of Internal Medicine, Mayo Clinic, Rochester, MN 55905, USA.
R M
RM
Taylor
S E
SE
Gabriel
W M
WM
O'Fallon
C A
CA
Bowles
J
J
Duffy
D000328:Adult; D000465:Algorithms; D002908:Chronic Disease; D003937:Diagnosis, Differential; D016603:Eosinophilia-Myalgia Syndrome / Q000175:diagnosis*; D005260:Female; D005356:Fibromyalgia / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009209:Myofascial Pain Syndromes / Q000175:diagnosis*