Functional dystonia: A case-control study and risk prediction algorithm.

OBJECTIVE

Functional dystonia (FD) is a disabling and diagnostically challenging functional movement disorder (FMD). We sought to identify historical predictors of FD vs. other primary dystonias (ODs) and develop a practical prediction algorithm to guide neurologists.

METHODS

1475 consecutive new patient medical records were reviewed at an adult/pediatric tertiary-referral dystonia clinic from 2005 to 2017. Ninety-nine met criteria for clinically established FD (85 adults and 14 pediatric), paired with 99 age/dystonia distribution-matched OD. Univariate and multivariate regression analyses were performed to identify predictors of FD and disability. We formed a prediction algorithm, assessed using the area under the receiver operating curve (AUC).

RESULTS

Multivariate logistic regression analysis investigating independent predictors of FD (P < 0.001) followed by development of a prediction algorithm showed that the most robust predictors included abrupt onset, spontaneous resolution/recurrence, pain, cognitive complaints, being on or pursuing disability, lifetime mood/anxiety disorder, comorbid functional somatic disorders, and having ≥3 medication allergies. The prediction algorithm had utility for both adult and pediatric FD, with excellent sensitivity/specificity (89%/92%) and an area under the curve (AUC) 0.95 (0.92-0.98). Greater disability (modified Rankin Scale) independently correlated with a number of functional examination features, unemployment/not attending school, number of medication allergies, and younger age of presentation. FD patients were high health-care utilizers and were more frequently prescribed opiates/opioids and benzodiazepines (P < 0.003).

INTERPRETATION

This case-control study provides an algorithm to guide clinicians in gauging their index of suspicion for a FD, with diagnostic confirmation subsequently informed by neurological examination. While this algorithm requires prospective validation, health-care utilization data underscore the importance and need for more research in FD.