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Updated: Oct 1, 2025
Computerized Adaptive Testing System of Functional Assessment of Stroke
Published on: January 7, 2019
Optimizing falls risk prediction for inpatient stroke rehabilitation: A secondary data analysis
Surekha Gangar1, Shajicaa Sivakumaran1, Ashley N Anderson1
1Department of Physical Therapy, University of Toronto, Toronto, ON, Canada.
Background:
Identifying individuals at risk for falls during inpatient stroke rehabilitation can ensure timely implementation of falls prevention strategies to minimize the negative personal and health system consequences of falls.
Objectives:
To compare sociodemographic and clinical characteristics of fallers and non-fallers; and evaluate the ability of the Berg Balance Scale (BBS) and Morse Falls Scale (MFS) to predict falls in an inpatient stroke rehabilitation setting.
Methods:
A longitudinal study involving a secondary analysis of health record data from 818 patients with stroke admitted to an urban, rehabilitation hospital was conducted. A fall was defined as having ≥1 fall during the hospital stay. Cut-points on the BBS and MFS, alone and in combination, that optimized sensitivity and specificity for predicting falls, were identified.
Results:
Low admission BBS score and admission to a low-intensity rehabilitation program were associated with falling (p < .05). Optimal cut-points were 29 for the BBS (sensitivity: 82.4%; specificity: 57.4%) and 30 for the MFS (sensitivity: 73.2%; specificity: 31.4%) when used alone. Cut-points of 45 (BBS) and 30 (MFS) in combination optimized sensitivity (74.1%) and specificity (42.7%).
Conclusions:
A BBS cut-point of 29 alone appears superior to using the MFS alone or combined with the BBS to predict falls.
