BACKGROUND: Individuals with stroke have a high risk of falling, and their fall predictors may differ from those of other populations. PURPOSE: To estimate fall frequency and identify factors related to fall occurrence in a sample of patients with stroke residing in the community. METHODS: Clinical data were collected from 150 consecutive stroke patients with independent gait, and the following scales were applied: modified Barthel Index (mBI), Timed Up & Go Test (TUG), and National Institutes of Health Stroke Scale (NIHSS). Univariate analysis was performed; variables with possible association (P < .1) were included in a logistic regression model. Receiver operating characteristic curves were used to identify the best cutoff point for TUG. RESULTS: Falls occurred in 37% of patients. In multivariate analysis, right hemisphere injury (odds ratio [OR], 2.621; 95% CI, 1.196-5.740; P = .016), time in TUG (OR, 1.035 for every increase in 1 second; 95% CI, 1.003-1.069; P = .034), and longer time since stroke onset (OR, 1.012 for every month increase; 95% CI, 1.002-1.021; P = .015) remained predictors. When we grouped individuals according to affected cerebral hemisphere, both hemispheres had similar accuracy, but TUG cutoff point was lower in individuals with right- versus left-hemisphere lesions. CONCLUSIONS: Patients with poor TUG performance, longer times since stroke onset, and right-hemisphere injury have particularly high fall rates, and TUG cutoff points for fall prediction vary according to cerebral hemisphere.
BACKGROUND: Individuals with stroke have a high risk of falling, and their fall predictors may differ from those of other populations. PURPOSE: To estimate fall frequency and identify factors related to fall occurrence in a sample of patients with stroke residing in the community. METHODS: Clinical data were collected from 150 consecutive strokepatients with independent gait, and the following scales were applied: modified Barthel Index (mBI), Timed Up & Go Test (TUG), and National Institutes of Health Stroke Scale (NIHSS). Univariate analysis was performed; variables with possible association (P < .1) were included in a logistic regression model. Receiver operating characteristic curves were used to identify the best cutoff point for TUG. RESULTS:Falls occurred in 37% of patients. In multivariate analysis, right hemisphere injury (odds ratio [OR], 2.621; 95% CI, 1.196-5.740; P = .016), time in TUG (OR, 1.035 for every increase in 1 second; 95% CI, 1.003-1.069; P = .034), and longer time since stroke onset (OR, 1.012 for every month increase; 95% CI, 1.002-1.021; P = .015) remained predictors. When we grouped individuals according to affected cerebral hemisphere, both hemispheres had similar accuracy, but TUG cutoff point was lower in individuals with right- versus left-hemisphere lesions. CONCLUSIONS:Patients with poor TUG performance, longer times since stroke onset, and right-hemisphere injury have particularly high fall rates, and TUG cutoff points for fall prediction vary according to cerebral hemisphere.
Authors: Aqeel M Alenazi; Mohammed M Alshehri; Shaima Alothman; Jason Rucker; Kari Dunning; Linda J D'Silva; Patricia M Kluding Journal: PM R Date: 2017-12-26 Impact factor: 2.298
Authors: Emma J Foster; Raphae S Barlas; Joao H Bettencourt-Silva; Allan B Clark; Anthony K Metcalf; Kristian M Bowles; John F Potter; Phyo K Myint Journal: Front Neurol Date: 2018-04-03 Impact factor: 4.003
Authors: Wycliffe E Wei; Deirdre A De Silva; Hui Meng Chang; Jiali Yao; David B Matchar; Sherry H Y Young; Siew Ju See; Gek Hsiang Lim; Ting Hway Wong; Narayanaswamy Venketasubramanian Journal: BMC Geriatr Date: 2019-12-26 Impact factor: 3.921
Authors: Husna Ahmad Ainuddin; Muhammad Hibatullah Romli; Tengku Aizan Hamid; Mazatulfazura S F Salim; Lynette Mackenzie Journal: Front Public Health Date: 2021-03-03