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Predicting Falls in People with Multiple Sclerosis: Fall History Is as Accurate as More Complex Measures

Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. Objective. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Methods. 52 people with MS were asked...

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Detalles Bibliográficos
Autores principales: Cameron, Michelle H., Thielman, Emily, Mazumder, Rajarshi, Bourdette, Dennis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3804404/
https://www.ncbi.nlm.nih.gov/pubmed/24191198
http://dx.doi.org/10.1155/2013/496325
Descripción
Sumario:Background. Many people with MS fall, but the best method for identifying those at increased fall risk is not known. Objective. To compare how accurately fall history, questionnaires, and physical tests predict future falls and injurious falls in people with MS. Methods. 52 people with MS were asked if they had fallen in the past 2 months and the past year. Subjects were also assessed with the Activities-specific Balance Confidence, Falls Efficacy Scale-International, and Multiple Sclerosis Walking Scale-12 questionnaires, the Expanded Disability Status Scale, Timed 25-Foot Walk, and computerized dynamic posturography and recorded their falls daily for the following 6 months with calendars. The ability of baseline assessments to predict future falls was compared using receiver operator curves and logistic regression. Results. All tests individually provided similar fall prediction (area under the curve (AUC) 0.60–0.75). A fall in the past year was the best predictor of falls (AUC 0.75, sensitivity 0.89, specificity 0.56) or injurious falls (AUC 0.69, sensitivity 0.96, specificity 0.41) in the following 6 months. Conclusion. Simply asking people with MS if they have fallen in the past year predicts future falls and injurious falls as well as more complex, expensive, or time-consuming approaches.