Ryan P Duncan1, James T Cavanaugh2, Gammon M Earhart3, Terry D Ellis4, Matthew P Ford5, K Bo Foreman6, Abigail L Leddy7, Serene S Paul8, Colleen G Canning9, Anne Thackeray6, Leland E Dibble10. 1. 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. 2. University of New England, Department of Physical Therapy, USA. 3. 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. 4. Boston University, Department of Physical Therapy and Athletic Training, USA. 5. University of Alabama at Birmingham School of Health Professions, Department of Physical Therapy, USA. 6. University of Utah, Department of Physical Therapy, USA. 7. Washington University School of Medicine in St. Louis, Program in Physical Therapy, USA. 8. University of Utah, Department of Physical Therapy, USA; The George Institute for Global Health, The University of Sydney, Sydney Medical School, Australia. 9. The University of Sydney, Faculty of Health Sciences, Australia. 10. University of Utah, Department of Physical Therapy, USA. Electronic address: Lee.dibble@hsc.utah.edu.
Abstract
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.
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.
Authors: Marjolein A van der Marck; Margit Ph C Klok; Michael S Okun; Nir Giladi; Marten Munneke; Bastiaan R Bloem Journal: Parkinsonism Relat Disord Date: 2013-12-30 Impact factor: 4.891
Authors: Serene S Paul; Colleen G Canning; Catherine Sherrington; Stephen R Lord; Jacqueline C T Close; Victor S C Fung Journal: Mov Disord Date: 2013-02-28 Impact factor: 10.338
Authors: Leland E Dibble; James T Cavanaugh; Gammon M Earhart; Terry D Ellis; Matthew P Ford; Kenneth B Foreman Journal: BMC Neurol Date: 2010-11-03 Impact factor: 2.474
Authors: Nir Giladi; Joseph Tal; Tali Azulay; Oliver Rascol; David J Brooks; Eldad Melamed; Wolfgang Oertel; Werner H Poewe; Fabrizio Stocchi; Eduardo Tolosa Journal: Mov Disord Date: 2009-04-15 Impact factor: 10.338
Authors: Ryan P Duncan; Abigail L Leddy; James T Cavanaugh; Leland E Dibble; Terry D Ellis; Matthew P Ford; K Bo Foreman; Gammon M Earhart Journal: Parkinsons Dis Date: 2011-11-30
Authors: Ewout W Steyerberg; Karel G M Moons; Danielle A van der Windt; Jill A Hayden; Pablo Perel; Sara Schroter; Richard D Riley; Harry Hemingway; Douglas G Altman Journal: PLoS Med Date: 2013-02-05 Impact factor: 11.069
Authors: Serene S Paul; Anne Thackeray; Ryan P Duncan; James T Cavanaugh; Theresa D Ellis; Gammon M Earhart; Matthew P Ford; K Bo Foreman; Leland E Dibble Journal: Arch Phys Med Rehabil Date: 2015-12-01 Impact factor: 3.966
Authors: Lorena Rosa S Almeida; Maria Elisa Pimentel Piemonte; Helen M Cavalcanti; Colleen G Canning; Serene S Paul Journal: Mov Disord Clin Pract Date: 2021-03-11
Authors: Christine Lo; Siddharth Arora; Fahd Baig; Michael A Lawton; Claire El Mouden; Thomas R Barber; Claudio Ruffmann; Johannes C Klein; Peter Brown; Yoav Ben-Shlomo; Maarten de Vos; Michele T Hu Journal: Ann Clin Transl Neurol Date: 2019-07-26 Impact factor: 4.511