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dc.contributor.authorJensen, R.
dc.contributor.authorKent, Peter
dc.contributor.authorJensen, T.
dc.contributor.authorKjaer, P.
dc.date.accessioned2018-05-18T08:01:05Z
dc.date.available2018-05-18T08:01:05Z
dc.date.created2018-05-18T00:23:27Z
dc.date.issued2018
dc.identifier.citationJensen, R. and Kent, P. and Jensen, T. and Kjaer, P. 2018. The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year old Danes. BMC Musculoskeletal Disorders. 19: 62.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/68186
dc.identifier.doi10.1186/s12891-018-1978-x
dc.description.abstract

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.

dc.publisherBiomed Central Ltd
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleThe association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year old Danes
dc.typeJournal Article
dcterms.source.volume19
dcterms.source.number62
dcterms.source.startPage1
dcterms.source.endPage12
dcterms.source.titleBMC Musculoskeletal Disorders
curtin.departmentSchool of Physiotherapy and Exercise Science
curtin.accessStatusOpen access


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