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, Siobh�n; Dunn, Kate M.

Abstract

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.

Citation

Ogollah, R. O., Konstantinou, K., Stynes, S., & Dunn, K. 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 and Research, 70(12), 1840-1848. https://doi.org/10.1002/acr.23556

• Journal Article Type: Article
• Acceptance Date: Mar 8, 2018
• Online Publication Date: Mar 25, 2018
• Publication Date: 2018-12
• Deposit Date: Mar 26, 2018
• Publicly Available Date: Mar 26, 2019
• Journal: Arthritis Care & Research
• Print ISSN: 2151-464X
• Electronic ISSN: 2151-4658
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 70
• Issue: 12
• Pages: 1840-1848
• DOI: https://doi.org/10.1002/acr.23556
• Keywords: Leg pain; pain trajectories; sciatica; primary care; growth mixture modelling; prospective
• Public URL: https://nottingham-repository.worktribe.com/output/921765
• Publisher URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/acr.23556
• Additional Information: This is the peer reviewed version of the following article: Ogollah, R. O., Konstantinou, K., Stynes, S. and Dunn, K. 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 Res. Accepted Author Manuscript, which has been published in final form at http://dx.doi.org/10.1002/acr.23556. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.