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Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke

Network meta-analysis is a method that can estimate relative efficacy between treatments that may not have been compared directly within the literature. The purpose of this study is to present a network meta-analysis of non-conventional interventions to improve upper extremity motor impairment after...

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Autores principales: Saikaley, Marcus, Pauli, Griffin, Sun, Hao, Serra, Julisa Rodriguez, Iruthayarajah, Jerome, Teasell, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698094/
https://www.ncbi.nlm.nih.gov/pubmed/36252104
http://dx.doi.org/10.1161/STROKEAHA.122.040687
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author Saikaley, Marcus
Pauli, Griffin
Sun, Hao
Serra, Julisa Rodriguez
Iruthayarajah, Jerome
Teasell, Robert
author_facet Saikaley, Marcus
Pauli, Griffin
Sun, Hao
Serra, Julisa Rodriguez
Iruthayarajah, Jerome
Teasell, Robert
author_sort Saikaley, Marcus
collection PubMed
description Network meta-analysis is a method that can estimate relative efficacy between treatments that may not have been compared directly within the literature. The purpose of this study is to present a network meta-analysis of non-conventional interventions to improve upper extremity motor impairment after stroke. METHODS: A literature search was conducted in 5 databases from their inception until April 1, 2021. Terms were used to narrow down articles related to stroke, the upper extremity, and interventional therapies. Randomized controlled trials written in English were eligible if; 50% poststroke patients; ≥18 years old; applied an intervention for the upper extremity, and/or used the Fugl-Meyer upper extremity scale as an outcome measure; the intervention had ≥3 randomized controlled trials with comparisons against a conventional care group; conventional care groups were dose matched for therapy time. A Bayesian network meta-analysis approach was taken to estimate mean difference (MD) and 95% CI. RESULTS: One hundred seventy-six randomized controlled trials containing 6781 participants examining 20 non-conventional interventions were identified for inclusion within the final model. Eight of the identified interventions proved significantly better than conventional care, with modified constraint induced movement therapy (MD, 6.7 [95% CI, 4.3–8.9]), high frequency repetitive transcranial magnetic stimulation (MD, 5.4 [95% CI, 1.9–8.9]), mental imagery (MD, 5.4 [95% CI, 1.8–8.9]), bilateral arm training (MD, 5.2 [95% CI, 2.2–8.1]), and intermittent theta-burst stimulation (MD, 5.1 [95% CI, 0.62–9.5]) occupying the top 5 spots according to the surface under the cumulative ranking curve. CONCLUSIONS: Overall, it would seem that modified constraint induced movement therapy has the greatest probability of being the most effective intervention, with high-frequency repetitive transcranial magnetic stimulation, mental imagery, and bilateral arm training all having similar probabilities of occupying the next spot in the rankings. We think this analysis can provide a guide for where future resources and clinical trials should be directed, and where a clinician may begin when considering alternative therapeutic interventions.
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spelling pubmed-96980942022-11-28 Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke Saikaley, Marcus Pauli, Griffin Sun, Hao Serra, Julisa Rodriguez Iruthayarajah, Jerome Teasell, Robert Stroke Original Contributions Network meta-analysis is a method that can estimate relative efficacy between treatments that may not have been compared directly within the literature. The purpose of this study is to present a network meta-analysis of non-conventional interventions to improve upper extremity motor impairment after stroke. METHODS: A literature search was conducted in 5 databases from their inception until April 1, 2021. Terms were used to narrow down articles related to stroke, the upper extremity, and interventional therapies. Randomized controlled trials written in English were eligible if; 50% poststroke patients; ≥18 years old; applied an intervention for the upper extremity, and/or used the Fugl-Meyer upper extremity scale as an outcome measure; the intervention had ≥3 randomized controlled trials with comparisons against a conventional care group; conventional care groups were dose matched for therapy time. A Bayesian network meta-analysis approach was taken to estimate mean difference (MD) and 95% CI. RESULTS: One hundred seventy-six randomized controlled trials containing 6781 participants examining 20 non-conventional interventions were identified for inclusion within the final model. Eight of the identified interventions proved significantly better than conventional care, with modified constraint induced movement therapy (MD, 6.7 [95% CI, 4.3–8.9]), high frequency repetitive transcranial magnetic stimulation (MD, 5.4 [95% CI, 1.9–8.9]), mental imagery (MD, 5.4 [95% CI, 1.8–8.9]), bilateral arm training (MD, 5.2 [95% CI, 2.2–8.1]), and intermittent theta-burst stimulation (MD, 5.1 [95% CI, 0.62–9.5]) occupying the top 5 spots according to the surface under the cumulative ranking curve. CONCLUSIONS: Overall, it would seem that modified constraint induced movement therapy has the greatest probability of being the most effective intervention, with high-frequency repetitive transcranial magnetic stimulation, mental imagery, and bilateral arm training all having similar probabilities of occupying the next spot in the rankings. We think this analysis can provide a guide for where future resources and clinical trials should be directed, and where a clinician may begin when considering alternative therapeutic interventions. Lippincott Williams & Wilkins 2022-10-14 2022-12 /pmc/articles/PMC9698094/ /pubmed/36252104 http://dx.doi.org/10.1161/STROKEAHA.122.040687 Text en © 2022 The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/Stroke is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made.
spellingShingle Original Contributions
Saikaley, Marcus
Pauli, Griffin
Sun, Hao
Serra, Julisa Rodriguez
Iruthayarajah, Jerome
Teasell, Robert
Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title_full Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title_fullStr Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title_full_unstemmed Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title_short Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
title_sort network meta-analysis of non-conventional therapies for improving upper limb motor impairment poststroke
topic Original Contributions
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698094/
https://www.ncbi.nlm.nih.gov/pubmed/36252104
http://dx.doi.org/10.1161/STROKEAHA.122.040687
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