Cargando…
Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring
OBJECTIVE: To evaluate the predictive validity of multimodal clinical assessment outcomes and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with different forms of neurological gait disorders. METHODS: The occurrence, severity, and consequences of fall...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357767/ https://www.ncbi.nlm.nih.gov/pubmed/33713194 http://dx.doi.org/10.1007/s00415-021-10504-x |
_version_ | 1783737202051645440 |
---|---|
author | Schniepp, Roman Huppert, Anna Decker, Julian Schenkel, Fabian Schlick, Cornelia Rasoul, Atal Dieterich, Marianne Brandt, Thomas Jahn, Klaus Wuehr, Max |
author_facet | Schniepp, Roman Huppert, Anna Decker, Julian Schenkel, Fabian Schlick, Cornelia Rasoul, Atal Dieterich, Marianne Brandt, Thomas Jahn, Klaus Wuehr, Max |
author_sort | Schniepp, Roman |
collection | PubMed |
description | OBJECTIVE: To evaluate the predictive validity of multimodal clinical assessment outcomes and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with different forms of neurological gait disorders. METHODS: The occurrence, severity, and consequences of falls were prospectively assessed for 6 months in 333 patients with early stage gait disorders due to vestibular, cerebellar, hypokinetic, vascular, functional, or other neurological diseases and 63 healthy controls. At inclusion, participants completed a comprehensive multimodal clinical and functional fall-risk assessment, an in-laboratory gait examination, and an inertial-sensor-based daily mobility monitoring for 14 days. Multivariate logistic regression analyses were performed to identify explanatory characteristics for predicting the (1) the fall status (non-faller vs. faller), (2) the fall frequency (occasional vs. frequent falls), and (3) the fall severity (benign vs. injurious fall) of patients. RESULTS: 40% of patients experienced one or frequent falls and 21% severe fall-related injuries during prospective fall assessment. Fall status and frequency could be reliably predicted (accuracy of 78 and 91%, respectively) primarily based on patients' retrospective fall status. Instrumented-based gait and mobility measures further improved prediction and provided independent, unique information for predicting the severity of fall-related consequences. INTERPRETATION: Falls- and fall-related injuries are a relevant health problem already in early stage neurological gait disorders. Multivariate regression analysis encourages a stepwise approach for fall assessment in these patients: fall history taking readily informs the clinician about patients' general fall risk. In patients at risk of falling, instrument-based measures of gait and mobility provide critical information on the likelihood of severe fall-related injuries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-021-10504-x. |
format | Online Article Text |
id | pubmed-8357767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-83577672021-08-30 Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring Schniepp, Roman Huppert, Anna Decker, Julian Schenkel, Fabian Schlick, Cornelia Rasoul, Atal Dieterich, Marianne Brandt, Thomas Jahn, Klaus Wuehr, Max J Neurol Original Communication OBJECTIVE: To evaluate the predictive validity of multimodal clinical assessment outcomes and quantitative measures of in- and off-laboratory mobility for fall-risk estimation in patients with different forms of neurological gait disorders. METHODS: The occurrence, severity, and consequences of falls were prospectively assessed for 6 months in 333 patients with early stage gait disorders due to vestibular, cerebellar, hypokinetic, vascular, functional, or other neurological diseases and 63 healthy controls. At inclusion, participants completed a comprehensive multimodal clinical and functional fall-risk assessment, an in-laboratory gait examination, and an inertial-sensor-based daily mobility monitoring for 14 days. Multivariate logistic regression analyses were performed to identify explanatory characteristics for predicting the (1) the fall status (non-faller vs. faller), (2) the fall frequency (occasional vs. frequent falls), and (3) the fall severity (benign vs. injurious fall) of patients. RESULTS: 40% of patients experienced one or frequent falls and 21% severe fall-related injuries during prospective fall assessment. Fall status and frequency could be reliably predicted (accuracy of 78 and 91%, respectively) primarily based on patients' retrospective fall status. Instrumented-based gait and mobility measures further improved prediction and provided independent, unique information for predicting the severity of fall-related consequences. INTERPRETATION: Falls- and fall-related injuries are a relevant health problem already in early stage neurological gait disorders. Multivariate regression analysis encourages a stepwise approach for fall assessment in these patients: fall history taking readily informs the clinician about patients' general fall risk. In patients at risk of falling, instrument-based measures of gait and mobility provide critical information on the likelihood of severe fall-related injuries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00415-021-10504-x. Springer Berlin Heidelberg 2021-03-13 2021 /pmc/articles/PMC8357767/ /pubmed/33713194 http://dx.doi.org/10.1007/s00415-021-10504-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Communication Schniepp, Roman Huppert, Anna Decker, Julian Schenkel, Fabian Schlick, Cornelia Rasoul, Atal Dieterich, Marianne Brandt, Thomas Jahn, Klaus Wuehr, Max Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title | Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title_full | Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title_fullStr | Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title_full_unstemmed | Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title_short | Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
title_sort | fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring |
topic | Original Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357767/ https://www.ncbi.nlm.nih.gov/pubmed/33713194 http://dx.doi.org/10.1007/s00415-021-10504-x |
work_keys_str_mv | AT schniepproman fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT huppertanna fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT deckerjulian fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT schenkelfabian fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT schlickcornelia fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT rasoulatal fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT dieterichmarianne fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT brandtthomas fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT jahnklaus fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring AT wuehrmax fallpredictioninneurologicalgaitdisordersdifferentialcontributionsfromclinicalassessmentgaitanalysisanddailylifemobilitymonitoring |