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The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes

BACKGROUND: Research into the clinical importance of spinal MRI findings in patients with low back pain (LBP) has primarily focused on single imaging findings, such as Modic changes or disc degeneration, and found only weak associations with the presence of pain. However, numerous MRI findings almos...

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Autores principales: Jensen, Rikke K., Kent, Peter, Jensen, Tue S., Kjaer, Per
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819254/
https://www.ncbi.nlm.nih.gov/pubmed/29463258
http://dx.doi.org/10.1186/s12891-018-1978-x
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author Jensen, Rikke K.
Kent, Peter
Jensen, Tue S.
Kjaer, Per
author_facet Jensen, Rikke K.
Kent, Peter
Jensen, Tue S.
Kjaer, Per
author_sort Jensen, Rikke K.
collection PubMed
description BACKGROUND: Research into the clinical importance of spinal MRI findings in patients with low back pain (LBP) has primarily focused on single imaging findings, such as Modic changes or disc degeneration, and found only weak associations with the presence of pain. However, numerous MRI findings almost always co-exist in the lumbar spine and are often present at more than one lumbar level. It is possible that multiple MRI findings are more strongly associated with LBP than single MRI findings. Latent Class Analysis is a statistical method that has recently been tested and found useful for identifying latent classes (subgroups) of MRI findings within multivariable datasets. The purpose of this study was to investigate the association between subgroups of MRI findings and the presence of LBP in people from the general population. METHODS: To identify subgroups of lumbar MRI findings with potential clinical relevance, Latent Class Analysis was initially performed on a clinical dataset of 631 patients seeking care for LBP. Subsequently, 412 participants in a general population cohort (the ‘Backs on Funen’ project) were statistically allocated to those existing subgroups by Latent Class Analysis, matching their MRI findings at a segmental level. The subgroups containing MRI findings from the general population were then organised into hypothetical pathways of degeneration and the association between subgroups in the pathways and the presence of LBP was tested using exact logistic regression. RESULTS: Six subgroups were identified in the clinical dataset and the data from the general population cohort fitted the subgroups well, with a median posterior probability of 93%–100%. These six subgroups described two pathways of increasing degeneration on upper (L1-L3) and lower (L4-L5) lumbar levels. An association with LBP was found for the subgroups describing severe and multiple degenerative MRI findings at the lower lumbar levels but none of the other subgroups were associated with LBP. CONCLUSION: Although MRI findings are common in asymptomatic people and the association between single MRI findings and LBP is often weak, our results suggest that subgroups of multiple and severe lumbar MRI findings have a stronger association with LBP than those with milder degrees of degeneration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12891-018-1978-x) contains supplementary material, which is available to authorized users.
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spelling pubmed-58192542018-02-21 The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes Jensen, Rikke K. Kent, Peter Jensen, Tue S. Kjaer, Per BMC Musculoskelet Disord Research Article BACKGROUND: Research into the clinical importance of spinal MRI findings in patients with low back pain (LBP) has primarily focused on single imaging findings, such as Modic changes or disc degeneration, and found only weak associations with the presence of pain. However, numerous MRI findings almost always co-exist in the lumbar spine and are often present at more than one lumbar level. It is possible that multiple MRI findings are more strongly associated with LBP than single MRI findings. Latent Class Analysis is a statistical method that has recently been tested and found useful for identifying latent classes (subgroups) of MRI findings within multivariable datasets. The purpose of this study was to investigate the association between subgroups of MRI findings and the presence of LBP in people from the general population. METHODS: To identify subgroups of lumbar MRI findings with potential clinical relevance, Latent Class Analysis was initially performed on a clinical dataset of 631 patients seeking care for LBP. Subsequently, 412 participants in a general population cohort (the ‘Backs on Funen’ project) were statistically allocated to those existing subgroups by Latent Class Analysis, matching their MRI findings at a segmental level. The subgroups containing MRI findings from the general population were then organised into hypothetical pathways of degeneration and the association between subgroups in the pathways and the presence of LBP was tested using exact logistic regression. RESULTS: Six subgroups were identified in the clinical dataset and the data from the general population cohort fitted the subgroups well, with a median posterior probability of 93%–100%. These six subgroups described two pathways of increasing degeneration on upper (L1-L3) and lower (L4-L5) lumbar levels. An association with LBP was found for the subgroups describing severe and multiple degenerative MRI findings at the lower lumbar levels but none of the other subgroups were associated with LBP. CONCLUSION: Although MRI findings are common in asymptomatic people and the association between single MRI findings and LBP is often weak, our results suggest that subgroups of multiple and severe lumbar MRI findings have a stronger association with LBP than those with milder degrees of degeneration. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12891-018-1978-x) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-20 /pmc/articles/PMC5819254/ /pubmed/29463258 http://dx.doi.org/10.1186/s12891-018-1978-x Text en © The Author(s). 2018 Open AccessThis 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 Article
Jensen, Rikke K.
Kent, Peter
Jensen, Tue S.
Kjaer, Per
The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title_full The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title_fullStr The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title_full_unstemmed The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title_short The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes
title_sort association between subgroups of mri findings identified with latent class analysis and low back pain in 40-year-old danes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819254/
https://www.ncbi.nlm.nih.gov/pubmed/29463258
http://dx.doi.org/10.1186/s12891-018-1978-x
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