Functional or not functional; that's the question Can we predict the diagnosis functional movement disorder based on associated features?

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 independentttest 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.

Automated summary

This study retrospectively reviewed medical records of all consecutive patients who visited a hyperkinetic outpatient clinic from 2012 to 2019 and compared 12 associated features between FMDs and non-FMDs. A preliminary predictive model was developed based on these differentiating features to distinguish between these disorders. The model showed a discriminative value of 91% in this large cohort.