Determining One-Year Trajectories of Low-Back-Related Leg Pain in Primary Care Patients: Growth Mixture Modeling of a Prospective Cohort Study

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 health care for low-back and leg pain, including sciatica, of any duration and severity, from their general practitioner. Growth mixture modeling was used to identify clusters of patients with distinct leg pain trajectories. Trajectories were characterized using baseline demographic and clinical examination data. Multinomial logistic regression was used to predict latent class membership, with a range of covariates.

Results: Four patient clusters were identified: improving mild pain (58%), persistent moderate pain (26%), persistent severe pain (13%), and improving severe pain (3%). Clusters showed statistically significant differences in 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.