Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Barons, Martine J, Griffiths, Frances E, Parsons, Nick, Alba, Anca, Thorogood, Margaret, Medley, Graham F, Lamb, Sarah E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282445/
https://www.ncbi.nlm.nih.gov/pubmed/24661395
http://dx.doi.org/10.1111/jep.12115
_version_ 1782351136784646144
author Barons, Martine J
Griffiths, Frances E
Parsons, Nick
Alba, Anca
Thorogood, Margaret
Medley, Graham F
Lamb, Sarah E
author_facet Barons, Martine J
Griffiths, Frances E
Parsons, Nick
Alba, Anca
Thorogood, Margaret
Medley, Graham F
Lamb, Sarah E
author_sort Barons, Martine J
collection PubMed
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.
format Online
Article
Text
id pubmed-4282445
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BlackWell Publishing Ltd
record_format MEDLINE/PubMed
spelling pubmed-42824452015-01-15 Matching patients to an intervention for back pain: classifying patients using a latent class approach Barons, Martine J Griffiths, Frances E Parsons, Nick Alba, Anca Thorogood, Margaret Medley, Graham F Lamb, Sarah E J Eval Clin Pract Forum on Systems and Complexity in Medicine and Healthcare 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. BlackWell Publishing Ltd 2014-08 2014-03-24 /pmc/articles/PMC4282445/ /pubmed/24661395 http://dx.doi.org/10.1111/jep.12115 Text en © 2014 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons, Ltd. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Forum on Systems and Complexity in Medicine and Healthcare
Barons, Martine J
Griffiths, Frances E
Parsons, Nick
Alba, Anca
Thorogood, Margaret
Medley, Graham F
Lamb, Sarah E
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
topic Forum on Systems and Complexity in Medicine and Healthcare
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282445/
https://www.ncbi.nlm.nih.gov/pubmed/24661395
http://dx.doi.org/10.1111/jep.12115
work_keys_str_mv AT baronsmartinej matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT griffithsfrancese matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT parsonsnick matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT albaanca matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT thorogoodmargaret matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT medleygrahamf matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach
AT lambsarahe matchingpatientstoaninterventionforbackpainclassifyingpatientsusingalatentclassapproach