Affiliations: [a] Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| [b] Center for Translational Neuroscience, University of Arkansas for Medical Sciences, Little Rock, AR, USA
Correspondence to: Tuhin Virmani, MD, PhD, University of Arkansas for Medical Sciences, 4301 W. Markham St., #500, Little Rock, AR 72205-7199, USA. Tel.: +1 501 686 7235; Fax: +1 501 686 8689; E-mail: [email protected].
Abstract: Background:Freezing of gait (FOG) is a debilitating feature of Parkinson’s disease (PD) for which treatments are limited. To develop neuroprotective strategies, determining whether disease progression is different in phenotypic variants of PD is essential. Objective:To determine if freezers have a faster decline in spatiotemporal gait parameters. Methods:Subjects were enrolled in a longitudinal study and assessed every 3– 6 months. Continuous gait in the levodopa ON-state was collected using a gait mat (Protokinetics). The slope of change/year in spatiotemporal gait parameters was calculated. Results:26 freezers, 31 non-freezers, and 25 controls completed an average of 6 visits over 28 months. Freezers had a faster decline in mean stride-length, stride-velocity, swing-%, single-support-%, and variability in single-support-% compared to non-freezers (p < 0.05). Gait decline was not correlated with initial levodopa dose, duration of levodopa therapy, change in levodopa dose or change in Montreal Cognitive Assessment scores (p > 0.25). Gait progression parameters were required to obtain 95% accuracy in categorizing FOG and noFOG groups in a forward step-wise binary regression model. Change in mean stride-length, mean stride-width, and swing-% variability along with initial foot-length variability, mean swing-% and apathy scores were significant variables in the model. Conclusion:Freezers had a faster temporal decline in objectively quantified gait, and inclusion of longitudinal gait changes in a binary regression model greatly increased categorization accuracy. Levodopa dosing, cognitive decline and disease severity were not significant in our model. Early detection of this differential decline may help define freezing prone groups for testing putative treatments.
Keywords: Freezing of gait, gait, falls, Parkinson’s disease, predictive modeling