Get access

Three simple clinical tests to accurately predict falls in people with Parkinson's disease

Authors

  • Serene S. Paul BAppSc(Phty)(Hons),

    Corresponding author
    • Clinical and Rehabilitation Sciences Research Group, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia
    Search for more papers by this author
  • Colleen G. Canning PhD,

    1. Clinical and Rehabilitation Sciences Research Group, Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia
    Search for more papers by this author
  • Catherine Sherrington PhD,

    1. Musculoskeletal Division, The George Institute for Global Health, The University of Sydney, Sydney, New South Wales, Australia
    Search for more papers by this author
  • Stephen R. Lord PhD, DSc,

    1. Neuroscience Research Australia, Sydney, New South Wales, Australia
    Search for more papers by this author
  • Jacqueline C. T. Close MD,

    1. Neuroscience Research Australia, Sydney, New South Wales, Australia
    2. Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia
    Search for more papers by this author
  • Victor S. C. Fung PhD, FRACP

    1. Movement Disorders Unit, Department of Neurology, Westmead Hospital, Sydney, New South Wales, Australia
    2. Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia
    Search for more papers by this author

  • Funding agencies: This study was supported by a National Health and Medical Research Council of Australia (NHMRC) Project Grant (ID: 512326), a grant from Perpetual Philanthropic Services, and a Parkinson's NSW research grant.

  • Relevant conflicts of interest/financial disclosures: S. S. Paul receives financial assistance from an NHMRC postgraduate scholarship. C. Sherrington and S. R. Lord receive salary funding from the NHMRC. S. R. Lord and J. C. T. Close have received an honorarium from Pfizer. V. S. C. Fung is on the advisory boards and/or has received travel grants from Abbott, Allergan, Boehringer-Ingelheim, Hospira, Lundbeck, and Novartis.

  • Full financial disclosures and author roles may be found in the online version of this article.

Correspondence to: Ms. S. S. Paul, Clinical and Rehabilitation Sciences Research Group, Faculty of Health Sciences, The University of Sydney, PO Box 170, Lidcombe NSW 1825, Australia; serene.paul@sydney.edu.au

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

Ancillary