Matching patients to an intervention for back pain: classifying patients using a latent class approach.
RATIONALE, AIMS AND OBJECTIVES: Classification 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 behav...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Blackwell Publishing Ltd
2014
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| _version_ | 1797058115128000512 |
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| author | Barons, M Griffiths, F Parsons, N Alba, A Thorogood, M Medley, G Lamb, S |
| author_facet | Barons, M Griffiths, F Parsons, N Alba, A Thorogood, M Medley, G Lamb, S |
| author_sort | Barons, M |
| collection | OXFORD |
| description | RATIONALE, AIMS AND OBJECTIVES: Classification 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. METHOD: We 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). RESULTS: Of 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. CONCLUSION: We 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. |
| first_indexed | 2024-03-06T19:45:57Z |
| format | Journal article |
| id | oxford-uuid:224d8552-f353-43b2-95de-b553163c6e8e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:45:57Z |
| publishDate | 2014 |
| publisher | Blackwell Publishing Ltd |
| record_format | dspace |
| spelling | oxford-uuid:224d8552-f353-43b2-95de-b553163c6e8e2022-03-26T11:38:04ZMatching patients to an intervention for back pain: classifying patients using a latent class approach.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:224d8552-f353-43b2-95de-b553163c6e8eEnglishSymplectic Elements at OxfordBlackwell Publishing Ltd2014Barons, MGriffiths, FParsons, NAlba, AThorogood, MMedley, GLamb, SRATIONALE, AIMS AND OBJECTIVES: Classification 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. METHOD: We 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). RESULTS: Of 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. CONCLUSION: We 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. |
| spellingShingle | Barons, M Griffiths, F Parsons, N Alba, A Thorogood, M Medley, G Lamb, S Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title | Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title_full | Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title_fullStr | Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title_full_unstemmed | Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title_short | Matching patients to an intervention for back pain: classifying patients using a latent class approach. |
| title_sort | matching patients to an intervention for back pain classifying patients using a latent class approach |
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