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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...

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Autores principales: Jonsdottir, Johanna, Mestanza Mattos, Fabiola Giovanna, Torchio, Alessandro, Corrini, Chiara, Cattaneo, Davide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174247/
https://www.ncbi.nlm.nih.gov/pubmed/37181569
http://dx.doi.org/10.3389/fneur.2023.1157453
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author Jonsdottir, Johanna
Mestanza Mattos, Fabiola Giovanna
Torchio, Alessandro
Corrini, Chiara
Cattaneo, Davide
author_facet Jonsdottir, Johanna
Mestanza Mattos, Fabiola Giovanna
Torchio, Alessandro
Corrini, Chiara
Cattaneo, Davide
author_sort Jonsdottir, Johanna
collection PubMed
description 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.
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spelling pubmed-101742472023-05-12 Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification Jonsdottir, Johanna Mestanza Mattos, Fabiola Giovanna Torchio, Alessandro Corrini, Chiara Cattaneo, Davide Front Neurol Neurology 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. Frontiers Media S.A. 2023-04-27 /pmc/articles/PMC10174247/ /pubmed/37181569 http://dx.doi.org/10.3389/fneur.2023.1157453 Text en Copyright © 2023 Jonsdottir, Mestanza Mattos, Torchio, Corrini and Cattaneo. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Jonsdottir, Johanna
Mestanza Mattos, Fabiola Giovanna
Torchio, Alessandro
Corrini, Chiara
Cattaneo, Davide
Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title_full Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title_fullStr Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title_full_unstemmed Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title_short Fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
title_sort fallers after stroke: a retrospective study to investigate the combination of postural sway measures and clinical information in faller’s identification
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174247/
https://www.ncbi.nlm.nih.gov/pubmed/37181569
http://dx.doi.org/10.3389/fneur.2023.1157453
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