Matching patients to an intervention for back pain: classifying patients using a latent class approach

Abstract

Rationale, aims and objectivesClassification of patients with back pain in order to inform treatments is a long-standing aim in medicine. We used latent class analysis (LCA) to classify patients with low back pain and investigate whether different classes responded differently to a cognitive behavioural intervention. The objective was to provide additional guidance on the use of cognitive behavioural therapy to both patients and clinicians.MethodWe used data from 407 participants from the full study population of 701 with complete data at baseline for the variables the intervention was designed to affect and complete data at 12 months for important outcomes. Patients were classified using LCA, and a link between class membership and outcome was investigated. For comparison, the latent class partition was compared with a commonly used classification system called Subgroups for Targeted Treatment (STarT).ResultsOf the relatively parsimonious models tested for association between class membership and outcome, an association was only found with one model which had three classes. For the trial participants who received the intervention, there was an association between class membership and outcome, but not for those who did not receive the intervention. However, we were unable to detect an effect on outcome from interaction between class membership and the intervention. The results from the comparative classification system were similar.ConclusionWe were able to classify the trial participants based on psychosocial baseline scores relevant to the intervention. An association between class membership and outcome was identified for those people receiving the intervention, but not those in the control group. However, we were not able to identify outcome associations for individual classes and so predict outcome in order to aid clinical decision making. For this cohort of patients, the STarT system was as successful, but not superior.

Differing Psychologically Derived Clusters in People With Chronic Low Back Pain are Associated With Different Multidimensional Profiles

“…As this study was cross-sectional, the nature and direction of associations between and within clusters is unknown. It is also unknown whether cluster membership predicts outcomes, however previous research involving psychologically-derived clusters would suggest this is likely [80][81][82] .…”

“…While direct comparison with other psychological cluster analysis studies is complicated by variability in the measures and clustering techniques used, similarities exist suggesting possible common psychological presentations. Numerous studies deriving two, three or four cluster solutions describe broadly similar low and high-scoring psychologically-derived clusters (acute / sub-acute [80][81][82] , variable duration (49% CLBP) 83 , CLBP [11][12] ). Consistent with our findings, lower-scoring CLBP clusters presented with lower anxiety and depression; higher pain self-efficacy and positive coping strategies [11][12] .…”

“…Three studies, with three or four cluster solutions, derived intermediate clusters scoring relatively low for affect (predominantly depression), and relatively high on fear-avoidance beliefs [80][81][82] ; also having Copyright © 2016 Wolters Kluwer Health, Inc. Unauthorized reproduction of the article is prohibited.…”

“…The broad range of profiling variables in this study is novel, having been limited in other cluster analysis studies to demographics, pain characteristics, employment, healthcare utilisation and comorbidities [11][12][80][81][82][83] . Such novel multidimensional profiling adds a new level of validation to the derived clusters 10 .…”

Using existing questionnaires in latent class analysis: should we use summary scores or single items as input? A methodological study using a cohort of patients with low back pain

“… 21 – 23 An exception was Baron et al, who found an association between LCA-derived baseline subgroups and their response to a cognitive behavioral intervention. 24 Their focus was on the psychological profile of patients at baseline. However, to better understand the complex nature of LBP, as well as any other complex health condition, and explore better options for the management of disease, it seems potentially fruitful to identify the clusters of associations between the broad arrays of biological, psychological, and social dimensions of patients, 25 as all these domains are present in individual patients.…”