Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study
Objective The clinical presentation and outcome of patients with back and leg pain in primary care are heterogeneous and may be better understood by identification of homogeneous and clinically meaningful subgroups. Subgroups of patients with different back pain trajectories have been identified,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/50683/ |
| _version_ | 1848798314005266432 |
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| author | Ogollah, Reuben O. Konstantinou, Kika Stynes, Siobhán Dunn, Kate M. |
| author_facet | Ogollah, Reuben O. Konstantinou, Kika Stynes, Siobhán Dunn, Kate M. |
| author_sort | Ogollah, Reuben O. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Objective
The clinical presentation and outcome of patients with back and leg pain in primary care are heterogeneous and may be better understood by identification of homogeneous and clinically meaningful subgroups. Subgroups of patients with different back pain trajectories have been identified, but little is known about the trajectories for patients with back‐related leg pain. This study sought to identify distinct leg pain trajectories, and baseline characteristics associated with membership of each group, in primary care patients.
Methods
Monthly data on leg pain intensity were collected over 12 months for 609 patients participating in a prospective cohort study of adult patients seeking healthcare for low back and leg pain including sciatica, of any duration and severity, from their general practitioner. Growth mixture modelling was used to identify clusters of patients with distinct leg pain trajectories. Trajectories were characterised using baseline demographic and clinical examination data. Multinomial logistic regression was used to predict latent class‐membership with a range of covariates.
Results
Four clusters were identified: (1) improving mild pain (58%), (2) persistent moderate pain (26%), (3) persistent severe pain (13%), and (4) improving severe pain (3%). Clusters showed statistically significant differences with a number of baseline characteristics.
Conclusion
Four trajectories of leg pain were identified. Clusters 1, 2 and 3 were generally comparable to back pain trajectories, while cluster 4, with major improvement in pain, is infrequently identified. Awareness of such distinct patient groups improves understanding of the course of leg pain and may provide a basis of classification for intervention. |
| first_indexed | 2025-11-14T20:17:48Z |
| format | Article |
| id | nottingham-50683 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:17:48Z |
| publishDate | 2018 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-506832019-03-25T04:30:11Z https://eprints.nottingham.ac.uk/50683/ Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study Ogollah, Reuben O. Konstantinou, Kika Stynes, Siobhán Dunn, Kate M. Objective The clinical presentation and outcome of patients with back and leg pain in primary care are heterogeneous and may be better understood by identification of homogeneous and clinically meaningful subgroups. Subgroups of patients with different back pain trajectories have been identified, but little is known about the trajectories for patients with back‐related leg pain. This study sought to identify distinct leg pain trajectories, and baseline characteristics associated with membership of each group, in primary care patients. Methods Monthly data on leg pain intensity were collected over 12 months for 609 patients participating in a prospective cohort study of adult patients seeking healthcare for low back and leg pain including sciatica, of any duration and severity, from their general practitioner. Growth mixture modelling was used to identify clusters of patients with distinct leg pain trajectories. Trajectories were characterised using baseline demographic and clinical examination data. Multinomial logistic regression was used to predict latent class‐membership with a range of covariates. Results Four clusters were identified: (1) improving mild pain (58%), (2) persistent moderate pain (26%), (3) persistent severe pain (13%), and (4) improving severe pain (3%). Clusters showed statistically significant differences with a number of baseline characteristics. Conclusion Four trajectories of leg pain were identified. Clusters 1, 2 and 3 were generally comparable to back pain trajectories, while cluster 4, with major improvement in pain, is infrequently identified. Awareness of such distinct patient groups improves understanding of the course of leg pain and may provide a basis of classification for intervention. Wiley 2018-03-25 Article PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/50683/1/Leg_pain_trajectories-V3.0-%20Revised%20Main%20Document_clean.pdf Ogollah, Reuben O., Konstantinou, Kika, Stynes, Siobhán and Dunn, Kate M. (2018) Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study. Arthritis Care & Research . ISSN 2151-4658 (In Press) Leg pain; pain trajectories; sciatica; primary care; growth mixture modelling; prospective https://onlinelibrary.wiley.com/doi/abs/10.1002/acr.23556 doi:10.1002/acr.23556 doi:10.1002/acr.23556 |
| spellingShingle | Leg pain; pain trajectories; sciatica; primary care; growth mixture modelling; prospective Ogollah, Reuben O. Konstantinou, Kika Stynes, Siobhán Dunn, Kate M. Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title | Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title_full | Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title_fullStr | Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title_full_unstemmed | Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title_short | Determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| title_sort | determining one-year trajectories of low back related leg pain in primary care patients: growth mixture modelling of a prospective cohort study |
| topic | Leg pain; pain trajectories; sciatica; primary care; growth mixture modelling; prospective |
| url | https://eprints.nottingham.ac.uk/50683/ https://eprints.nottingham.ac.uk/50683/ https://eprints.nottingham.ac.uk/50683/ |