Abstract
BackgroundHeterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed. Latent Class Analysis (LCA) is a statistical technique that is increasingly being used to identify subgroups based on patient characteristics. However, as LBP is a complex multi-domain condition, the optimal approach when using LCA is unknown. Therefore, this paper describes the exploration of two approaches to LCA that may help improve the identification of clinically relevant and interpretable LBP subgroups.MethodsFrom 928 LBP patients consulting a chiropractor, baseline data were used as input to the statistical subgrouping. In a single-stage LCA, all variables were modelled simultaneously to identify patient subgroups. In a two-stage LCA, we used the latent class membership from our previously published LCA within each of six domains of health (activity, contextual factors, pain, participation, physical impairment and psychology) (first stage) as the variables entered into the second stage of the two-stage LCA to identify patient subgroups. The description of the results of the single-stage and two-stage LCA was based on a combination of statistical performance measures, qualitative evaluation of clinical interpretability (face validity) and a subgroup membership comparison.ResultsFor the single-stage LCA, a model solution with seven patient subgroups was preferred, and for the two-stage LCA, a nine patient subgroup model. Both approaches identified similar, but not identical, patient subgroups characterised by (i) mild intermittent LBP, (ii) recent severe LBP and activity limitations, (iii) very recent severe LBP with both activity and participation limitations, (iv) work-related LBP, (v) LBP and several negative consequences and (vi) LBP with nerve root involvement.ConclusionsBoth approaches identified clinically interpretable patient subgroups. The potential importance of these subgroups needs to be investigated by exploring whether they can be identified in other cohorts and by examining their possible association with patient outcomes. This may inform the selection of a preferred LCA approach.
Highlights
Heterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed
We inspected the most appealing candidate models on their: (1) subgroup size, as we favoured Latent Class Analysis (LCA) models in which the smallest subgroup size was at least 5% of the whole cohort; (2) conditional probabilities for categorical and ordinal items; (3) conditional means of ordinal and continuous items; and (4) loadings [46]
Data were available from 928 participants and 95% of these had more than 86% complete data
Summary
Heterogeneity in patients with low back pain (LBP) is well recognised and different approaches to subgrouping have been proposed. Much research conducted during this period has not applied the biopsychosocial model, as studies often focus on only one aspect of the model [14] One reason for this could be uncertainties about how to handle the complexity and volume of data that can arise as a consequence of this expanded focus. It is a stratification tool which traverses the pain, activity limitation and psychology domains using a simple 9-item questionnaire to guide management of the heterogeneity in LBP [6]. This has some promise for improving treatment effects and there are models for estimating LBP prognoses [15], much of the heterogeneity in LBP patients is still poorly understood [16]
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