Optimizing falls risk prediction for inpatient stroke rehabilitation: A secondary data analysis

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.