Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller's identification.

Background: Falls can have devastating effects on quality of life. No clear relationships have been identified between clinical and stabilometric postural measures and falling in persons after stroke. Objective: This cross-sectional study investigates the value of including stabilometric measures of sway with clinical measures of balance in models for identification of faller chronic stroke survivors, and the relations between variables. Methods: Clinical and stabilometric data were collected from a convenience sample of 49 persons with stroke in hospital care. They were categorized as fallers ( Results: The area under the curve (AUC) for model 1 was 0.68 (95%CI: 0.53-0.83, sensitivity = 95%, specificity = 39%) with prediction accuracy of 63.3%. Model 2 resulted in an AUC of 0.68 (95%CI: 0.53-0.84, sensitivity = 76%, specificity = 57%) with prediction accuracy of 65.3%. The AUC of stepwise model 3 was 0.74 (95%CI: 0.60-0.88, sensitivity = 57%, specificity = 81%) with prediction accuracy of 67.4%. Finally, statistically significant correlations were found between clinical variables ( Conclusion: A model combining BBS, BI, and SwayML was best at identifying faller status in persons in the chronic phase post stroke. When balance performance is poor, a high SwayML may be part of a strategy protecting from falls.