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...

Descripción completa

Detalles Bibliográficos
Autores principales: Schniepp, Roman, Huppert, Anna, Decker, Julian, Schenkel, Fabian, Schlick, Cornelia, Rasoul, Atal, Dieterich, Marianne, Brandt, Thomas, Jahn, Klaus, Wuehr, Max
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