Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity?
MetadataShow full item record
Background: Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables'). Methods: This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above. Results: The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up. Conclusions: Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.
Showing items related by title, author, creator and subject.
Identifying subgroups of patients using Latent Class Analysis: Should we use a single-stage or a two-stage approach? A methodological study using a cohort of patients with low back painNielsen, A.; Kent, Peter; Vach, W.; Kongsted, A. (2017)Background Heterogeneity 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 ...
The role of functional, radiological and self-reported measures in predicting clinical outcome in spondylotic cervical radiculopathyAgarwal, Shabnam (2011)BackgroundCervical radiculopathy (CR) results in significant disability and pain and is commonly treated conservatively with satisfactory clinical outcomes. However, a considerable number of patients require surgery to ...
Identification of subgroups of inflammatory and degenerative MRI findings in the axial skeleton: A latent class analysis of 1,037 patients with persistent low back painArnbak, B.; Jensen, R.; Manniche, C.; Hendricks, O.; Kent, Peter; Jurik, A.; Jensen, T. (2016)Background: The aim of this study was to investigate subgroups of magnetic resonance imaging (MRI) findings for the spine and sacroiliac joints (SIJs) using latent class analysis (LCA), and to investigate whether these ...