PMID- 36396075
OWN - NLM
STAT- MEDLINE
DCOM- 20230130
LR  - 20230213
IS  - 1878-5921 (Electronic)
IS  - 0895-4356 (Linking)
VI  - 153
DP  - 2023 Jan
TI  - Data-driven network analysis identified subgroup-specific low back pain pathways: 
      a cross-sectional GLA:D Back study.
PG  - 66-77
LID - S0895-4356(22)00290-6 [pii]
LID - 10.1016/j.jclinepi.2022.11.010 [doi]
AB  - OBJECTIVES: To understand the physical, activity, pain, and psychological 
      pathways contributing to low back pain (LBP) -related disability, and if these 
      differ between subgroups. METHODS: Data came from the baseline observations 
      (n = 3849) of the "GLA:D Back" intervention program for long-lasting nonspecific 
      LBP. 15 variables comprising demographic, pain, psychological, physical, 
      activity, and disability characteristics were measured. Clustering was used for 
      subgrouping, Bayesian networks (BN) were used for structural learning, and 
      structural equation model (SEM) was used for statistical inference. RESULTS: Two 
      clinical subgroups were identified with those in subgroup 1 having worse symptoms 
      than those in subgroup 2. Psychological factors were directly associated with 
      disability in both subgroups. For subgroup 1, psychological factors were most 
      strongly associated with disability (beta = 0.363). Physical factors were directly 
      associated with disability (beta = -0.077), and indirectly via psychological 
      factors. For subgroup 2, pain was most strongly associated with disability 
      (beta = 0.408). Psychological factors were common predictors of physical factors 
      (beta = 0.078), pain (beta = 0.518), activity (beta = -0.101), and disability (beta = 0.382). 
      CONCLUSIONS: The importance of psychological factors in both subgroups suggests 
      their importance for treatment. Differences in the interaction between physical, 
      pain, and psychological factors and their contribution to disability in different 
      subgroups may open the doors toward more optimal LBP treatments.
CI  - Copyright (c) 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
FAU - Liew, Bernard X W
AU  - Liew BXW
AD  - School of Sport, Rehabilitation and Exercise Sciences, University of Essex, 
      Colchester, Essex, UK. Electronic address: bl19622@essex.ac.uk.
FAU - Hartvigsen, Jan
AU  - Hartvigsen J
AD  - Department of Sports Science and Clinical Biomechanics, University of Southern 
      Denmark, Odense, Denmark; Chiropractic Knowledge Hub, Odense, Denmark.
FAU - Scutari, Marco
AU  - Scutari M
AD  - Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Lugano, 
      Switzerland.
FAU - Kongsted, Alice
AU  - Kongsted A
AD  - Department of Sports Science and Clinical Biomechanics, University of Southern 
      Denmark, Odense, Denmark; Chiropractic Knowledge Hub, Odense, Denmark.
LA  - eng
PT  - Journal Article
DEP - 20221115
PL  - United States
TA  - J Clin Epidemiol
JT  - Journal of clinical epidemiology
JID - 8801383
SB  - IM
MH  - Humans
MH  - *Low Back Pain/diagnosis
MH  - Cross-Sectional Studies
MH  - Bayes Theorem
MH  - *Chronic Pain
MH  - Cluster Analysis
MH  - Disability Evaluation
OTO - NOTNLM
OT  - Bayesian networks
OT  - Chronic pain
OT  - Low back pain
OT  - Machine learning
OT  - Network analysis
OT  - Structural equation modeling
EDAT- 2022/11/18 06:00
MHDA- 2023/01/31 06:00
CRDT- 2022/11/17 19:32
PHST- 2022/09/12 00:00 [received]
PHST- 2022/11/02 00:00 [revised]
PHST- 2022/11/09 00:00 [accepted]
PHST- 2022/11/18 06:00 [pubmed]
PHST- 2023/01/31 06:00 [medline]
PHST- 2022/11/17 19:32 [entrez]
AID - S0895-4356(22)00290-6 [pii]
AID - 10.1016/j.jclinepi.2022.11.010 [doi]
PST - ppublish
SO  - J Clin Epidemiol. 2023 Jan;153:66-77. doi: 10.1016/j.jclinepi.2022.11.010. Epub 
      2022 Nov 15.