Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning

Tagliaferri, Scott D; Wilkin, Tim; Angelova, Maia; Fitzgibbon, Bernadette M.; Owen, Patrick J; Miller, Clint T.; Belavý, Daniel L.

doi:10.1038/s41598-022-19542-5

Cited by 9 publications

(15 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tagliaferri et all. (25) used a univariate approach for initial variable selection before clustering cLBP patients. This has the risk of discarding variables from the analysis that are informative for clustering when combined with other variables.…”

Section: Discussionmentioning

confidence: 99%

“…Tagliaferri et all. (25) found 5 subgroups of cLBP patients, mostly divided with reference to depressive symptoms and social isolation. Our data did not contain detailed measurements of psychosocial factors, which could have limited partitioning subgroups into further clusters.…”

Section: Discussionmentioning

confidence: 99%

“…A recent work by Tagliaferri et all. (25) demonstrated that subgroups of cLBP patients can be discovered respect to a pain-free cohort using a combination of variable selection and clustering methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised subgrouping of chronic low back pain patients treated in a specialty clinic

Torres-Espin,

Keller,

Ewing

et al. 2023

Preprint

View full text Add to dashboard Cite

BackgroundChronic low back pain (cLBP) is the leading cause of disability worldwide. Current treatments have minor or moderate effects, partly because of the idiopathic nature of most cLBP cases, the complexity of its presentation, and heterogeneity in the population. Explaining this complexity and heterogeneity by identifying subgroups of patients is critical for personalized health. Clinical decisions tailoring treatment to patients’ subgroup characteristics and specific treatment responses can improve health outcomes. Current patient stratification tools divide cases into subgroups based on a small subset of characteristics, which may not capture many factors determining patient phenotypes.Methods and FindingsIn this study, we use an unsupervised machine learning framework to identify patient subgroups within a specialized back pain clinic and evaluate their outcomes. Our analysis identified 25 latent factors determining patient phenotypes and found three distinctive clusters of patients. The research suggests that there is heterogeneity in the population of patients treated in a specialty setting and that several factors determine patient phenotypes. Cluster 1 consists of those individuals with characteristics found to be protective of chronic pain: younger age, low pain medication prescription, high function, good insurance access, and low overlapping pain conditions. Individuals in Cluster 3 associate with older age and present with a higher incidence of chronic overlapping pain conditions, comorbidities, and pain medication use. Cluster 2 is an intermediate group.ConclusionsWe quantify cLBP population heterogeneity and demonstrate how ML analytical workflow can be used to explain, in part, this heterogeneity in relation to outcomes. Notably, considering a data-driven approach from multi-domain data produces different subgroups than the STarT back screening tool, and the addition of other functional metrics at baseline such as global physical and mental function, and pain intensity, increases the variance explained in outcomes. Our study provides novel insights into the complex nature of cLBP and the potential for data-driven methods to identify clinically relevant subtypes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised subgrouping of chronic low back pain patients treated in a specialty clinic

Torres-Espin,

Keller,

Ewing

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A 3T MRI scanner with a 32 channel receive-only phased-array head coil will be used. As in preliminary work [ 31 , 47 ], high resolution magnetisation prepared rapid acquisition gradient echo (MP2RAGE) T1-weighted structural data will be acquired to assess grey matter volumes. Resting state functional connectivity in pain networks will be assessed using an EPI sequence and those data will be pre-processed, denoised and analysed using standardized pipelines.…”

Section: Methodsmentioning

confidence: 99%

“…There is an evidence gap for a study to use data-science techniques in LBP in an appropriate sample size. Since this review, work from our group [ 31 ], using data from the UK Biobank, showed that sub-groups could be derived in (chronic) LBP on the basis of psychosocial variables and then accurately classified, but the data set lacked spinal tissue and pain sensitisation measures and key clinical data (e.g. pain intensity, disability and pain duration).…”

Section: Introductionmentioning

confidence: 99%

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

Belavy,

Tagliaferri,

Tegenthoff

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The “PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain” (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18–55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs.

show abstract

Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study

Tagliaferri,

Owen,

Miller

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings.

show abstract

Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning

Cited by 9 publications

References 62 publications

Unsupervised subgrouping of chronic low back pain patients treated in a specialty clinic

Unsupervised subgrouping of chronic low back pain patients treated in a specialty clinic

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study

Contact Info

Product

Resources

About