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Framework for improving outcome prediction for acute to chronic low back pain transitions
Clinical practice guidelines and the Federal Pain Research Strategy (United States) have recently highlighted research priorities to lessen the public health impact of low back pain (LBP). It may be necessary to improve existing predictive approaches to meet these research priorities for the transit...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7209816/ https://www.ncbi.nlm.nih.gov/pubmed/32440606 http://dx.doi.org/10.1097/PR9.0000000000000809 |
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author | George, Steven Z. Lentz, Trevor A. Beneciuk, Jason M. Bhavsar, Nrupen A. Mundt, Jennifer M. Boissoneault, Jeff |
author_facet | George, Steven Z. Lentz, Trevor A. Beneciuk, Jason M. Bhavsar, Nrupen A. Mundt, Jennifer M. Boissoneault, Jeff |
author_sort | George, Steven Z. |
collection | PubMed |
description | Clinical practice guidelines and the Federal Pain Research Strategy (United States) have recently highlighted research priorities to lessen the public health impact of low back pain (LBP). It may be necessary to improve existing predictive approaches to meet these research priorities for the transition from acute to chronic LBP. In this article, we first present a mapping review of previous studies investigating this transition and, from the characterization of the mapping review, present a predictive framework that accounts for limitations in the identified studies. Potential advantages of implementing this predictive framework are further considered. These advantages include (1) leveraging routinely collected health care data to improve prediction of the development of chronic LBP and (2) facilitating use of advanced analytical approaches that may improve prediction accuracy. Furthermore, successful implementation of this predictive framework in the electronic health record would allow for widespread testing of accuracy resulting in validated clinical decision aids for predicting chronic LBP development. |
format | Online Article Text |
id | pubmed-7209816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer |
record_format | MEDLINE/PubMed |
spelling | pubmed-72098162020-05-21 Framework for improving outcome prediction for acute to chronic low back pain transitions George, Steven Z. Lentz, Trevor A. Beneciuk, Jason M. Bhavsar, Nrupen A. Mundt, Jennifer M. Boissoneault, Jeff Pain Rep Musculoskeletal Clinical practice guidelines and the Federal Pain Research Strategy (United States) have recently highlighted research priorities to lessen the public health impact of low back pain (LBP). It may be necessary to improve existing predictive approaches to meet these research priorities for the transition from acute to chronic LBP. In this article, we first present a mapping review of previous studies investigating this transition and, from the characterization of the mapping review, present a predictive framework that accounts for limitations in the identified studies. Potential advantages of implementing this predictive framework are further considered. These advantages include (1) leveraging routinely collected health care data to improve prediction of the development of chronic LBP and (2) facilitating use of advanced analytical approaches that may improve prediction accuracy. Furthermore, successful implementation of this predictive framework in the electronic health record would allow for widespread testing of accuracy resulting in validated clinical decision aids for predicting chronic LBP development. Wolters Kluwer 2020-03-04 /pmc/articles/PMC7209816/ /pubmed/32440606 http://dx.doi.org/10.1097/PR9.0000000000000809 Text en Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain. This is an open access article distributed under the Creative Commons Attribution-NoDerivatives License 4.0 (CC BY-ND) (http://creativecommons.org/licenses/by-nd/4.0/) which allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author. |
spellingShingle | Musculoskeletal George, Steven Z. Lentz, Trevor A. Beneciuk, Jason M. Bhavsar, Nrupen A. Mundt, Jennifer M. Boissoneault, Jeff Framework for improving outcome prediction for acute to chronic low back pain transitions |
title | Framework for improving outcome prediction for acute to chronic low back pain transitions |
title_full | Framework for improving outcome prediction for acute to chronic low back pain transitions |
title_fullStr | Framework for improving outcome prediction for acute to chronic low back pain transitions |
title_full_unstemmed | Framework for improving outcome prediction for acute to chronic low back pain transitions |
title_short | Framework for improving outcome prediction for acute to chronic low back pain transitions |
title_sort | framework for improving outcome prediction for acute to chronic low back pain transitions |
topic | Musculoskeletal |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7209816/ https://www.ncbi.nlm.nih.gov/pubmed/32440606 http://dx.doi.org/10.1097/PR9.0000000000000809 |
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