Functional or not functional; that's the question: Can we predict the diagnosis functional movement disorder based on associated features?. (20th September 2020)

• Record Type: Journal Article
• Authors: Lagrand, T.; Tuitert, I.; Klamer, M.; van der Meulen, A.; van der Palen, J.; Kramer, G.; Tijssen, M.
• Is Part Of: European journal of neurology. Volume 28:Number 1(2021)
• Page Start: 33
• Page End: 39
• Publication Date: 2020-09-20
• Subjects: associated features -- clinical characteristics -- FMD -- functional movement disorders -- prediction model; Neurology -- Periodicals; Nervous system -- Diseases -- Periodicals; 616.8
• DOI: 10.1111/ene.14488
• Languages: English
• ISSNs: 1351-5101

Abstract

Abstract : Background and purpose: Functional movement disorders (FMDs) pose a diagnostic challenge for clinicians. Over the years several associated features have been shown to be suggestive for FMDs. Which features mentioned in the literature are discriminative between FMDs and non‐FMDs were examined in a large cohort. In addition, a preliminary prediction model distinguishing these disorders was developed based on differentiating features. Method: Medical records of all consecutive patients who visited our hyperkinetic outpatient clinic from 2012 to 2019 were retrospectively reviewed and 12 associated features in FMDs versus non‐FMDs were compared. An independent t test for age of onset and Pearson chi‐squared analyses for all categorical variables were performed. Multivariate logistic regression analysis was performed to develop a preliminary predictive model for FMDs. Results: A total of 874 patients were eligible for inclusion, of whom 320 had an FMD and 554 a non‐FMD. Differentiating features between these groups were age of onset, sex, psychiatric history, family history, more than one motor phenotype, pain, fatigue, abrupt onset, waxing and waning over long term, and fluctuations during the day. Based on these a preliminary predictive model was computed with a discriminative value of 91%. Discussion: Ten associated features are shown to be not only suggestive but also discriminative between hyperkinetic FMDs and non‐FMDs. Clinicians can use these features to identify patients suspected for FMDs and can subsequently alert them to test for positive symptoms at examination. Although a first preliminary model has good predictive accuracy, further validation should be performed prospectively in a multi‐center study.