Do kinematic gait parameters help to discriminate between fallers and non-fallers with Parkinson's disease?. Issue 2 (February 2021)

• Record Type: Journal Article
• Authors: Delval, Arnaud; Betrouni, Nacim; Tard, Céline; Devos, David; Dujardin, Kathy; Defebvre, Luc; Labidi, Jordan; Moreau, Caroline
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 2(2021)
• Page Start: 536
• Page End: 541
• Publication Date: 2021-02
• Subjects: Gait -- Falls -- Parkinson's disease -- Freezing of gait; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2020.11.027
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Accuracy of clinical evaluation for distinguishing between fallers and non-fallers was of 94%. Incorporating additional gait parameters improved the accuracy of up to 97%. Foot clearance appeared useful for distinguishing between fallers and non-fallers. Abstract: Objective: Although a number of clinical factors have been linked to falls in Parkinson's disease (PD), the diagnostic value of gait parameters remains subject to debate. The objective of this retrospective study was to determine to what extent the combination of gait parameters with clinical characteristics can distinguish between fallers and non-fallers. Methods: Using a video motion system, we recorded gait in 174 patients with PD. The patients' clinical characteristics (including motor status, cognitive status, disease duration, dopaminergic treatment and any history of falls or freezing of gait) were noted. The considered kinematic gait parameters included indices of gait bradykinesia and hypokinesia, asymmetry, variability, and foot clearance. After a parameters selection using an ANCOVA analysis, support vector machine algorithm was used to build classification models for distinguishing between fallers and non-fallers. Two models were built, the first included clinical data only while the second incorporated the selected gait parameters. Results: The "clinical-only" model had an accuracy of 94% for distinguishing between fallers and non-fallers. The model incorporating additional gait parameters including stride time and foot clearance performed even better, with an accuracy of up to 97%. Conclusion: Although fallers differed significantly from non-fallers with regard to disease duration, motor impairment or dopaminergic treatment, the addition of gait parameters such as foot clearance or stride time to clinical variables increased the model's discriminant power. Significance: This predictive model now needs to be validated in prospective cohorts.