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

 If your therapists aren't constantly perturbing your walking to increase your ability to prevent falls, you don't have the best therapists.

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

Johanna Jonsdottir^1*, Fabiola Giovanna Mestanza Mattos², Alessandro Torchio¹, Chiara Corrini¹ and Davide Cattaneo^1,2

• ¹IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
• ²Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy

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 (N = 21) or non-fallers (N = 28) based on the occurrence of falls in the previous 6 months. Logistic regression (model 1) was performed with clinical measures, including the Berg Balance scale (BBS), Barthel Index (BI), and Dynamic Gait Index (DGI). A second model (model 2) was run with stabilometric measures, including mediolateral (SwayML) and anterior–posterior sway (SwayAP), velocity of antero-posterior (VelAP) and medio-lateral sway (VelML), and absolute position of center of pressure (CopX abs). A third stepwise regression model was run including all variables, resulting in a model with SwayML, BBS, and BI (model 3). Finally, correlations between independent variables were analyzed.

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 (p < 0.05), only velocity parameters were correlated with balance performance (p < 0.05).

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.

Introduction

People with hemiparesis following stroke have various neuromotor and sensory disorders that can lead to balance problems and falls during activities of daily living. Their risk of falling is up to triple that of an age-matched population, making fall prevention an important healthcare goal (1–4). Accurate identification of the pathological and functional factors contributing to balance disorders in persons with stroke is of utmost importance in providing adequate appropriate treatment and reducing the risk of falls (5). Postural control and balance have been extensively studied with clinical measures concerning risk of falling (3, 6–10). Simpson and colleagues followed a study population for 1 year after a stroke and found that balance was the only common independent predictor of falls in persons with stroke (11). The difference in fall rates could be explained by the difference in balance scores on clinical scales. Similarly, Mackintosh and colleagues reported reduced mobility and balance in recurrent faller’s post-stroke (9), while on the contrary Hyndman and colleagues found no differences between fallers and non-fallers using clinical scales (10).

Overall, clinical measures focusing on balance performance have proven to be only moderately good at identifying fallers or those at risk of falling. However, since balance with its underlying body functions is a complex construct it is possible that adding information from objective balance control measures might give a more complete picture of balance. This would improve our understanding of factors most likely to impact on fall risk in persons post stroke (12). Stabilometric platform measures give information on weight bearing symmetry, amount of sway, and velocity of sway during quiet standing and may give added insight into the specific underlying abnormalities in postural control and the consequential imbalance leading to falls. There are some indications that stabilometric measures related to mediolateral sway and velocity of sway, are associated with falls in healthy elderly persons, with several studies reporting an association of falls with increased mediolateral sway and increased velocity of antero-posterior sway with eyes open and closed in that population (13–18). Differences have been found in weight bearing symmetry and postural sway parameters between healthy subjects and persons with stroke, with the latter having larger and faster sway, especially in the frontal plane (10, 19–21). However, studies on the relationship of these postural impairments to the occurrence of falls in persons with stroke have reported rather ambivalent results (22). Sackley et al. found a significant relationship between increased body sway and the number of falls in persons with stroke, with however, only a small amount of the variation in the number of falls explained by body sway (23). In a study by Jørgensen instead, larger body sway did not result being a significant risk factor for falls (1). Similarly, in a recent study, Bower and colleagues found that quiet standing body sway parameters did not predict falls in the subacute phase after stroke (24). On the other hand, Lee and Jung reported postural sway at 3 months post-stroke as contributing to increased risk of falls at around 1-year post-stroke (25). As is evident, no clear relationships have been identified between postural sway impairments and falling in persons in the chronic phase after stroke and, to our knowledge, no studies have put together clinical and stabilometric measures in faller prediction models for that population (25, 26). Given the importance of improving detection of fallers and identification of potential fall risk markers, the primary aim of this study was to investigate the relative accuracy of commonly used clinical measures in stroke for identifying faller status, and the added value of quantitative measures of postural sway in quiet standing. For that purpose, we included measures of balance and mobility performance, as well as, as well as quantitative measures of postural sway in predictive models. Further, associations between clinical and stabilometric variables were explored.

More at link.