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Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach

BACKGROUND: Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations betw...

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Autores principales: Steele, Katherine M., Schwartz, Michael H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204855/
https://www.ncbi.nlm.nih.gov/pubmed/35721346
http://dx.doi.org/10.3389/fnhum.2022.846205
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author Steele, Katherine M.
Schwartz, Michael H.
author_facet Steele, Katherine M.
Schwartz, Michael H.
author_sort Steele, Katherine M.
collection PubMed
description BACKGROUND: Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations between impairments and movement, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility. The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS). METHODS: We evaluated the impact of SEMLS on change in Gait Deviation Index (ΔGDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, ΔGDI, baseline GDI (GDI(pre)), baseline neurologic and orthopedic impairments (Imp(pre)), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on ΔGDI and the impact of Imp(pre) on ΔGDI and GDI(pre). We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects. RESULTS: We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N = 55, 35 males, age: 10.5 ± 3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N = 55, 30 males, age: 10.0 ± 3.4 years). There was a small positive causal effect of SEMLS on ΔGDI (1.70 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDI(pre), but minimal effects on ΔGDI. Spasticity and orthopedic impairments had minimal effects on GDI(pre) or ΔGDI. CONCLUSION: Altered motor control did have a strong effect on GDI(pre), indicating that these impairments do have a causal effect on a child’s gait pattern, but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP.
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spelling pubmed-92048552022-06-18 Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach Steele, Katherine M. Schwartz, Michael H. Front Hum Neurosci Human Neuroscience BACKGROUND: Altered motor control is common in cerebral palsy (CP). Understanding how altered motor control affects movement and treatment outcomes is important but challenging due to complex interactions with other neuromuscular impairments. While regression can be used to examine associations between impairments and movement, causal modeling provides a mathematical framework to specify assumed causal relationships, identify covariates that may introduce bias, and test model plausibility. The goal of this research was to quantify the causal effects of altered motor control and other impairments on gait, before and after single-event multi-level orthopedic surgery (SEMLS). METHODS: We evaluated the impact of SEMLS on change in Gait Deviation Index (ΔGDI) between gait analyses. We constructed our causal model with a Directed Acyclic Graph that included the assumed causal relationships between SEMLS, ΔGDI, baseline GDI (GDI(pre)), baseline neurologic and orthopedic impairments (Imp(pre)), age, and surgical history. We identified the adjustment set to evaluate the causal effect of SEMLS on ΔGDI and the impact of Imp(pre) on ΔGDI and GDI(pre). We used Bayesian Additive Regression Trees (BART) and accumulated local effects to assess relative effects. RESULTS: We prospectively recruited a cohort of children with bilateral CP undergoing SEMLS (N = 55, 35 males, age: 10.5 ± 3.1 years) and identified a control cohort with bilateral CP who did not undergo SEMLS (N = 55, 30 males, age: 10.0 ± 3.4 years). There was a small positive causal effect of SEMLS on ΔGDI (1.70 GDI points). Altered motor control (i.e., dynamic and static motor control) and strength had strong effects on GDI(pre), but minimal effects on ΔGDI. Spasticity and orthopedic impairments had minimal effects on GDI(pre) or ΔGDI. CONCLUSION: Altered motor control did have a strong effect on GDI(pre), indicating that these impairments do have a causal effect on a child’s gait pattern, but minimal effect on expected changes in GDI after SEMLS. Heterogeneity in outcomes suggests there are other factors contributing to changes in gait. Identifying these factors and employing causal methods to examine the complex relationships between impairments and movement will be required to advance our understanding and care of children with CP. Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9204855/ /pubmed/35721346 http://dx.doi.org/10.3389/fnhum.2022.846205 Text en Copyright © 2022 Steele and Schwartz. 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 Human Neuroscience
Steele, Katherine M.
Schwartz, Michael H.
Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title_full Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title_fullStr Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title_full_unstemmed Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title_short Causal Effects of Motor Control on Gait Kinematics After Orthopedic Surgery in Cerebral Palsy: A Machine-Learning Approach
title_sort causal effects of motor control on gait kinematics after orthopedic surgery in cerebral palsy: a machine-learning approach
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9204855/
https://www.ncbi.nlm.nih.gov/pubmed/35721346
http://dx.doi.org/10.3389/fnhum.2022.846205
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