Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity?
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. Previ...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Biomed Central Ltd
2017
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| Online Access: | http://hdl.handle.net/20.500.11937/55939 |
| _version_ | 1848759745065779200 |
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| author | Molgaard Nielsen, A. Hestbaek, L. Vach, W. Kent, Peter Kongsted, A. |
| author_facet | Molgaard Nielsen, A. Hestbaek, L. Vach, W. Kent, Peter Kongsted, A. |
| author_sort | Molgaard Nielsen, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:04:46Z |
| format | Journal Article |
| id | curtin-20.500.11937-55939 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:04:46Z |
| publishDate | 2017 |
| publisher | Biomed Central Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-559392017-10-19T00:41:12Z Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? Molgaard Nielsen, A. Hestbaek, L. Vach, W. Kent, Peter Kongsted, A. 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. 2017 Journal Article http://hdl.handle.net/20.500.11937/55939 10.1186/s12891-017-1708-9 http://creativecommons.org/licenses/by/4.0/ Biomed Central Ltd fulltext |
| spellingShingle | Molgaard Nielsen, A. Hestbaek, L. Vach, W. Kent, Peter Kongsted, A. Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title | Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title_full | Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title_fullStr | Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title_full_unstemmed | Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title_short | Latent class analysis derived subgroups of low back pain patients - Do they have prognostic capacity? |
| title_sort | latent class analysis derived subgroups of low back pain patients - do they have prognostic capacity? |
| url | http://hdl.handle.net/20.500.11937/55939 |