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Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls (n = 14,927) from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables (loneliness/social isolation and depression) and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35-53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls.

BACKGROUND: Effectiveness of implementing interventions to optimise guideline-recommended medical prescription in low back pain is not well established. METHODS: A systematic review and random-effects meta-analyses for dichotomous outcomes with a Paule-Mandel estimator. Five databases and reference lists were searched from inception to 4th August 2021. Randomised controlled/clinical trials in adults with low back pain to optimise medication prescription were included. Cochrane Risk of Bias 2 tool and GRADE were implemented. The review was registered prospectively with PROSPERO (CRD42020219767). FINDINGS: Of 3352 unique records identified in the search, seven studies were included and five were eligible for meta-analysis (N=11339 participants). Six of seven studies incorporated clinician education, three studies included audit/feedback components and one study implemented changes in medical records systems. Via meta-analysis, we estimated a non-significant odds-ratio of 0·94 (95% CI (0·77; 1.16), I² = 0%; n=5 studies, GRADE: low) in favour of the intervention group. The main finding was robust to sensitivity analyses. INTERPRETATION: There is low quality evidence that existing interventions to optimise medication prescription or usage in back pain had no impact. Peer-to-peer education alone does not appear to lead to behaviour change. Organisational and policy interventions may be more effective. FUNDING: This work was supported by internal institutional funding only.