Objective: To evaluate relative accuracy of a newly developed Stroke Assessment of Fall Risk (SAFR) for classifying fallers and non-fallers, compared with a health system fall risk screening tool, the Fall Harm Risk Screen.
Design And Setting: Prospective quality improvement study conducted at an inpatient stroke rehabilitation unit at a large urban university hospital.
Participants: Patients admitted for inpatient stroke rehabilitation (N = 419) with imaging or clinical evidence of ischemic or hemorrhagic stroke, between 1 August 2009 and 31 July 2010.
Interventions: Not applicable.
Main Outcome Measures: Sensitivity, specificity, and area under the curve for Receiver Operating Characteristic Curves of both scales' classifications, based on fall risk score completed upon admission to inpatient stroke rehabilitation.
Results: A total of 68 (16%) participants fell at least once. The SAFR was significantly more accurate than the Fall Harm Risk Screen (p < 0.001), with area under the curve of 0.73, positive predictive value of 0.29, and negative predictive value of 0.94. For the Fall Harm Risk Screen, area under the curve was 0.56, positive predictive value was 0.19, and negative predictive value was 0.86. Sensitivity and specificity of the SAFR (0.78 and 0.63, respectively) was higher than the Fall Harm Risk Screen (0.57 and 0.48, respectively).
Conclusions: An evidence-derived, population-specific fall risk assessment may more accurately predict fallers than a general fall risk screen for stroke rehabilitation patients. While the SAFR improves upon the accuracy of a general assessment tool, additional refinement may be warranted.
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http://dx.doi.org/10.1177/0269215514534276 | DOI Listing |
Geriatr Gerontol Int
January 2025
Division of Acute Care Surgery, Department of Surgery, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa, USA.
Aim: Pre-injury frailty has been investigated as a tool to predict outcomes of older trauma patients. Using artificial intelligence principles of machine learning, we aimed to identify a "signature" (combination of clinical variables) that could predict which older adults are at risk of fall-related hospital admission. We hypothesized that frailty, measured using the 5-item modified Frailty Index, could be utilized in combination with other factors as a predictor of admission for fall-related injuries.
View Article and Find Full Text PDFInj Prev
January 2025
The University of Queensland, Brisbane, Queensland, Australia.
Background: Given that fall injury is a critical public health concern in Australia, understanding the economic implications of falls among older adults is crucial to allocating healthcare resources efficiently to reduce falls and improve quality of life. This study therefore aimed to estimate the cost and identify factors associated with fall-related injuries within residential aged care (RAC).
Methods: A cohort analysis from the healthcare system perspective based on data from a double-blinded randomised controlled trial-the Opti-Med trial.
J Biomech
January 2025
Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, USA. Electronic address:
Most often, gait biomechanics is studied during straight-ahead walking. However, real-life walking imposes various lateral maneuvers people must navigate. Such maneuvers challenge people's lateral balance and can induce falls.
View Article and Find Full Text PDFQJM
January 2025
Tallaght hospital, Dept. of Age Related Healthcare; Trinity College Dublin, Dept. of Medical Gerontology.
Background: Falls are frequently reported within the HSE. The Irish Longitudinal Study on Ageing(TILDA) found that 40% of over 50 s experience a fall in a two year period, with 20% requiring hospital attendance (1). It has been estimated that the cost of injuries related to falls in older people will increase exponentially over the coming years (2).
View Article and Find Full Text PDFGait Posture
January 2025
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan, Taiwan. Electronic address:
Background: The use of inertial measurement units (IMUs) in assessing fall risk is often limited by subject discomfort and challenges in data interpretation. Additionally, there is a scarcity of research on attitude estimation features. To address these issues, we explored novel features and representation methods in the context of sit-to-stand transitions.
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