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Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology

BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of s...

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Autores principales: Lee, Sunghoon I., Campion, Andrew, Huang, Alex, Park, Eunjeong, Garst, Jordan H., Jahanforouz, Nima, Espinal, Marie, Siero, Tiffany, Pollack, Sophie, Afridi, Marwa, Daneshvar, Meelod, Ghias, Saif, Sarrafzadeh, Majid, Lu, Daniel C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516369/
https://www.ncbi.nlm.nih.gov/pubmed/28720144
http://dx.doi.org/10.1186/s12984-017-0288-0
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author Lee, Sunghoon I.
Campion, Andrew
Huang, Alex
Park, Eunjeong
Garst, Jordan H.
Jahanforouz, Nima
Espinal, Marie
Siero, Tiffany
Pollack, Sophie
Afridi, Marwa
Daneshvar, Meelod
Ghias, Saif
Sarrafzadeh, Majid
Lu, Daniel C.
author_facet Lee, Sunghoon I.
Campion, Andrew
Huang, Alex
Park, Eunjeong
Garst, Jordan H.
Jahanforouz, Nima
Espinal, Marie
Siero, Tiffany
Pollack, Sophie
Afridi, Marwa
Daneshvar, Meelod
Ghias, Saif
Sarrafzadeh, Majid
Lu, Daniel C.
author_sort Lee, Sunghoon I.
collection PubMed
description BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes. METHODS: This study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables. RESULTS: The gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10(−7)) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10(−4)). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated. CONCLUSIONS: The reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-017-0288-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-55163692017-07-20 Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology Lee, Sunghoon I. Campion, Andrew Huang, Alex Park, Eunjeong Garst, Jordan H. Jahanforouz, Nima Espinal, Marie Siero, Tiffany Pollack, Sophie Afridi, Marwa Daneshvar, Meelod Ghias, Saif Sarrafzadeh, Majid Lu, Daniel C. J Neuroeng Rehabil Research BACKGROUND: Approximately 33% of the patients with lumbar spinal stenosis (LSS) who undergo surgery are not satisfied with their postoperative clinical outcomes. Therefore, identifying predictors for postoperative outcome and groups of patients who will benefit from the surgical intervention is of significant clinical benefit. However, many of the studied predictors to date suffer from subjective recall bias, lack fine digital measures, and yield poor correlation to outcomes. METHODS: This study utilized smart-shoes to capture gait parameters extracted preoperatively during a 10 m self-paced walking test, which was hypothesized to provide objective, digital measurements regarding the level of gait impairment caused by LSS symptoms, with the goal of predicting postoperative outcomes in a cohort of LSS patients who received lumbar decompression and/or fusion surgery. The Oswestry Disability Index (ODI) and predominant pain level measured via the Visual Analogue Scale (VAS) were used as the postoperative clinical outcome variables. RESULTS: The gait parameters extracted from the smart-shoes made statistically significant predictions of the postoperative improvement in ODI (RMSE =0.13, r=0.93, and p<3.92×10(−7)) and predominant pain level (RMSE =0.19, r=0.83, and p<1.28×10(−4)). Additionally, the gait parameters produced greater prediction accuracy compared to the clinical variables that had been previously investigated. CONCLUSIONS: The reported results herein support the hypothesis that the measurement of gait characteristics by our smart-shoe system can provide accurate predictions of the surgical outcomes, assisting clinicians in identifying which LSS patient population can benefit from the surgical intervention and optimize treatment strategies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12984-017-0288-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-18 /pmc/articles/PMC5516369/ /pubmed/28720144 http://dx.doi.org/10.1186/s12984-017-0288-0 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lee, Sunghoon I.
Campion, Andrew
Huang, Alex
Park, Eunjeong
Garst, Jordan H.
Jahanforouz, Nima
Espinal, Marie
Siero, Tiffany
Pollack, Sophie
Afridi, Marwa
Daneshvar, Meelod
Ghias, Saif
Sarrafzadeh, Majid
Lu, Daniel C.
Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title_full Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title_fullStr Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title_full_unstemmed Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title_short Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
title_sort identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516369/
https://www.ncbi.nlm.nih.gov/pubmed/28720144
http://dx.doi.org/10.1186/s12984-017-0288-0
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