External validation of a simple clinical tool used to predict falls in people with Parkinson disease.

Autor: Duncan RP; Washington University School of Medicine in St. Louis, Program in Physical Therapy, USA; Washington University School of Medicine in St. Louis, Department of Neurology, USA., Cavanaugh JT; University of New England, Department of Physical Therapy, USA., Earhart GM; Washington University School of Medicine in St. Louis, Program in Physical Therapy, USA; Washington University School of Medicine in St. Louis, Department of Neurology, USA; Washington University School of Medicine in St. Louis, Department of Anatomy & Neurobiology, USA., Ellis TD; Boston University, Department of Physical Therapy and Athletic Training, USA., Ford MP; University of Alabama at Birmingham School of Health Professions, Department of Physical Therapy, USA., Foreman KB; University of Utah, Department of Physical Therapy, USA., Leddy AL; Washington University School of Medicine in St. Louis, Program in Physical Therapy, USA., Paul SS; University of Utah, Department of Physical Therapy, USA; The George Institute for Global Health, The University of Sydney, Sydney Medical School, Australia., Canning CG; The University of Sydney, Faculty of Health Sciences, Australia., Thackeray A; University of Utah, Department of Physical Therapy, USA., Dibble LE; University of Utah, Department of Physical Therapy, USA. Electronic address: Lee.dibble@hsc.utah.edu.
Jazyk: angličtina
Zdroj: Parkinsonism & related disorders [Parkinsonism Relat Disord] 2015 Aug; Vol. 21 (8), pp. 960-3. Date of Electronic Publication: 2015 May 16.
DOI: 10.1016/j.parkreldis.2015.05.008
Abstrakt: Background: Assessment of fall risk in an individual with Parkinson disease (PD) is a critical yet often time consuming component of patient care. Recently a simple clinical prediction tool based only on fall history in the previous year, freezing of gait in the past month, and gait velocity <1.1 m/s was developed and accurately predicted future falls in a sample of individuals with PD.
Methods: We sought to externally validate the utility of the tool by administering it to a different cohort of 171 individuals with PD. Falls were monitored prospectively for 6 months following predictor assessment.
Results: The tool accurately discriminated future fallers from non-fallers (area under the curve [AUC] = 0.83; 95% CI 0.76-0.89), comparable to the developmental study.
Conclusion: The results validated the utility of the tool for allowing clinicians to quickly and accurately identify an individual's risk of an impending fall.
(Copyright © 2015 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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