Heterogeneity in Measures of Illness among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Is Not Explained by Clinical Practice: A Study in Seven U.S. Specialty Clinics.
Elizabeth R UngerJin-Mann S LinYang ChenMonica E CorneliusBritany HeltonAnindita N IssaJeanne BertolliNancy G KlimasElizabeth G BalbinLucinda BatemanCharles W LappWendy SpringsRichard N PodellTrisha FitzpatrickDaniel L PetersonCarl Gunnar GottschalkBenjamin H NatelsonMichelle BlateAndreas M KogelnikCatrina C Phannull On Behalf Of The McAm Study GroupPublished in: Journal of clinical medicine (2024)
Background: One of the goals of the Multi-site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM) study was to evaluate whether clinicians experienced in diagnosing and caring for patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) recognized the same clinical entity. Methods: We enrolled participants from seven specialty clinics in the United States. We used baseline data (n = 465) on standardized questions measuring general clinical characteristics, functional impairment, post-exertional malaise, fatigue, sleep, neurocognitive/autonomic symptoms, pain, and other symptoms to evaluate whether patient characteristics differed by clinic. Results: We found few statistically significant and no clinically significant differences between clinics in their patients' standardized measures of ME/CFS symptoms and function. Strikingly, patients in each clinic sample and overall showed a wide distribution in all scores and measures. Conclusions: Illness heterogeneity may be an inherent feature of ME/CFS. Presenting research data in scatter plots or histograms will help clarify the challenge. Relying on case-control study designs without subgrouping or stratification of ME/CFS illness characteristics may limit the reproducibility of research findings and could obscure underlying mechanisms.
Keyphrases
- sleep quality
- primary care
- end stage renal disease
- case report
- chronic kidney disease
- ejection fraction
- newly diagnosed
- clinical practice
- physical activity
- prognostic factors
- peritoneal dialysis
- electronic health record
- depressive symptoms
- deep learning
- big data
- machine learning
- blood pressure
- neuropathic pain
- spinal cord injury
- pain management
- drug induced
- palliative care
- patient reported outcomes
- artificial intelligence