Two symptoms can accurately identify post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Background: Post-exertional malaise (PEM) is the hallmark symptom of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) yet its diverse manifestations make it difficult to recognize. Brief instruments for detecting PEM are critical for clinical and scientific progress.

Objective: To develop a clinical prediction rule for PEM.

Method: 49 ME/CFS and 10 healthy, sedentary subjects recruited from the community completed two maximal cardiopulmonary exercise tests (CPETs) separated by 24 hours.

At five different times, subjects reported symptoms which were then classified into 19 categories. The frequency of symptom reports between groups at each time point was compared using Fisher’s exact test.

Receiver operating characteristics (ROC) analysis with area under the curve calculation was used to determine the number of different types of symptom reports that were sufficient to differentiate between ME/CFS and sedentary groups. The optimal number of symptoms was determined where sensitivity and specificity of the types of symptom reports were balanced.

Results: At all timepoints, a maximum of two symptoms was optimal to determine differences between groups. Only one symptom was necessary to optimally differentiate between groups at one week following the second CPET. Fatigue, cognitive dysfunction, lack of positive feelings/mood and decrease in function were consistent predictors of ME/CFS group membership across timepoints.

Conclusion: Inquiring about post-exertional cognitive dysfunction, decline in function, and lack of positive feelings/mood may help identify PEM quickly and accurately. These findings should be validated with a larger sample of patients.

Source: Davenport, Todd E; Chu, Lily; Stevens, Staci R; Stevens, Jared; Snell, Christopher R; Van Ness, J. Mark. Two symptoms can accurately identify post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome. Work. 1 Jan. 2023 : 1 – 15. https://content.iospress.com/articles/work/wor220554 (Full text)

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