Data-driven network analysis identified subgroup-specific low back pain pathways: a cross-sectional GLA:D Back study

Abstract

Objectives: To understand the physical, activity, pain, and psychological pathways contributing to low back pain (LBP) -related disability, and if these differ between subgroups.

Methods: Data came from the baseline observations (n = 3849) of the "GLA:D Back" intervention program for long-lasting nonspecific LBP. 15 variables comprising demographic, pain, psychological, physical, activity, and disability characteristics were measured. Clustering was used for subgrouping, Bayesian networks (BN) were used for structural learning, and structural equation model (SEM) was used for statistical inference.

Results: Two clinical subgroups were identified with those in subgroup 1 having worse symptoms than those in subgroup 2. Psychological factors were directly associated with disability in both subgroups. For subgroup 1, psychological factors were most strongly associated with disability (β = 0.363). Physical factors were directly associated with disability (β = -0.077), and indirectly via psychological factors. For subgroup 2, pain was most strongly associated with disability (β = 0.408). Psychological factors were common predictors of physical factors (β = 0.078), pain (β = 0.518), activity (β = -0.101), and disability (β = 0.382).

Conclusions: The importance of psychological factors in both subgroups suggests their importance for treatment. Differences in the interaction between physical, pain, and psychological factors and their contribution to disability in different subgroups may open the doors toward more optimal LBP treatments.

Keywords: Bayesian networks; Chronic pain; Low back pain; Machine learning; Network analysis; Structural equation modeling.