Citation

Paul SS, Canning CG, Sherrington C, Lord SR, Close JC, Fung VS. Mov. Disord. 2013; 28(5): 655-662.

Affiliation

Clinical and Rehabilitation Sciences Research Group, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia.

Copyright

(Copyright © 2013, Movement Disorders Society, Publisher John Wiley and Sons)

DOI

10.1002/mds.25404

PMID

23450694

Abstract

Falls are a major cause of morbidity in Parkinson's disease (PD). The objective of this study was to identify predictors of falls in PD and develop a simple prediction tool that would be useful in routine patient care. Potential predictor variables (falls history, disease severity, cognition, leg muscle strength, balance, mobility, freezing of gait [FOG], and fear of falling) were collected for 205 community-dwelling people with PD. Falls were monitored prospectively for 6 months using monthly falls diaries. In total, 125 participants (59%) fell during follow-up. A model that included a history of falls, FOG, impaired postural sway, gait speed, sit-to-stand, standing balance with narrow base of support, and coordinated stability had high discrimination in identifying fallers (area under the receiver-operating characteristic curve [AUC], 0.83; 95% confidence interval [CI], 0.77-0.88). A clinical tool that incorporated 3 predictors easily determined in a clinical setting (falling in the previous year: odds ratio [OR], 5.80; 95% CI, 3.00-11.22; FOG in the past month: OR, 2.39; 95% CI, 1.19-4.80; and self-selected gait speed < 1.1 meters per second: OR, 1.86; 95% CI, 0.96-3.58) had similar discrimination (AUC, 0.80; 95% CI, 0.73-0.86) to the more complex model (P = 0.14 for comparison of AUCs). The absolute probability of falling in the next 6 months for people with low, medium, and high risk using the simple, 3-test tool was 17%, 51%, and 85%, respectively. In people who have PD without significant cognitive impairment, falls can be predicted with a high degree of accuracy using a simple, 3-test clinical tool. This tool enables individualized quantification of the risk of falling. © 2013 Movement Disorder Society.

Language: en