NDLI: Latent class analysis derived subgroups of low back pain patients

Content Provider	Springer Nature : BioMed Central
Author	Molgaard Nielsen, Anne Hestbaek, Lise Vach, Werner Kent, Peter Kongsted, Alice
Abstract	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.
Related Links	https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-017-1708-9.pdf
Ending Page	19
Page Count	19
Starting Page	1
File Format	HTM / HTML
ISSN	14712474
DOI	10.1186/s12891-017-1708-9
Journal	BMC Musculoskeletal Disorders
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2017-08-09
Access Restriction	Open
Subject Keyword	Orthopedics Rehabilitation Rheumatology Sports Medicine Internal Medicine Epidemiology Low back pain Subgrouping Classification prognosis Prospective studies Latent class analysis
Content Type	Text
Resource Type	Article
Subject	Orthopedics and Sports Medicine Rheumatology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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 pain

The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes

How can latent trajectories of back pain be translated into defined subgroups?

Prognostic implications of the Quebec Task Force classification of back-related leg pain: an analysis of longitudinal routine clinical data

Recovery trajectories in common musculoskeletal complaints by diagnosis contra prognostic phenotypes

Identifying latent subgroups in the older population seeking primary health care for a new episode of back pain – findings from the BACE-N cohort

Chronic low back pain patient groups in primary care – A cross sectional cluster analysis

Classification of patients with low back-related leg pain: a systematic review

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Latent class analysis derived subgroups of low back pain patients – do they have prognostic capacity?

Similar Documents

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 pain

The association between subgroups of MRI findings identified with latent class analysis and low back pain in 40-year-old Danes

How can latent trajectories of back pain be translated into defined subgroups?

Prognostic implications of the Quebec Task Force classification of back-related leg pain: an analysis of longitudinal routine clinical data

Recovery trajectories in common musculoskeletal complaints by diagnosis contra prognostic phenotypes

Identifying latent subgroups in the older population seeking primary health care for a new episode of back pain – findings from the BACE-N cohort

Chronic low back pain patient groups in primary care – A cross sectional cluster analysis

Classification of patients with low back-related leg pain: a systematic review

Neck pain patterns and subgrouping based on weekly SMS-derived trajectories

Latent class analysis derived subgroups of low back pain patients – do they have prognostic capacity?