Relative contributions of the nervous system, spinal tissue and psychosocial health to non‐specific low back pain: Multivariate meta‐analysis

Abstract

Background and Objectives: Nervous system, psychosocial and spinal tissue biomarkers are associated with non-specific low back pain (nsLBP), though relative contributions are unclear. Databases and DataTreatment: MEDLINE, EMBASE, CINAHL, PsycINFO and SPORTDiscus were searched up to 25 March 2020. Related reviews and reference lists were also screened. Observational studies examining structural and functional nervous system biomarkers (e.g. quantitative sensory tests, structural and functional brain measures), psychosocial factors (e.g. mental health, catastrophizing) and structural spinal imaging biomarkers (e.g. intervertebral discdegeneration, paraspinal muscle size) between nsLBP and pain-free controls were included. For multivariate meta-analysis, two of three domains were required in each study. Random-effects pairwise and multivariate meta-analyses were performed. GRADE approach assessed evidence certainty. Newcastle-Ottawa scale assessed risk of bias. Main outcomes were the effect size difference of domains between nsLBP and pain-free controls.Results: Of 4519 unique records identified, 33 studies (LBP = 1552, referents = 1322) were meta-analysed. Psychosocial state (Hedges' g [95%CI]: 0.90 [0.69-1.10], p < 0.001) in nsLBP showed larger effect sizes than nervous system (0.

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

“…From the average within- and between-cluster distances, we also calculated the discrimination value between clusters as reported here 58 . Given the potential for differences in effect sizes/variance in outcomes across domains due to the way they are measured, even when they are all scaled to 0–1, we also explored clustering for variables which passed prior steps in each sub-domain of psychosocial, physical, brain function and brain structure separately 59 .…”

“…The reason for completing classification with the addition of pain-free controls was to determine if and what sub-groups were separable from pain-free controls. There is contention surrounding the most important pain-related features, therefore, determining which features separate CBP from pain-free controls is important to guide future research to find specific treatable phenotypes of the condition 59 . Furthermore, we attempted classification on the sub-groups derived from the main and sub-domain clustering analyses.…”

The power of integrating data: advancing pain research using meta-analysis

“…Tagliaferri et al analysed the contributions of multiple factors (pathological and psychological biomarkers) related to persistent nonspecific low back pain. 82 They concluded that there were significant contributions of all studied biomarker categories to persistent low back pain (nervous system, spinal imaging, and psychosocial). However, psychosocial factors showed the greatest effect (Hedges g = 0.90, 95% CI: 0.69–1.10) compared with the nervous system (Hedges g = 0.31, 95% CI: 0.13–0.49) or spinal imaging measures (Hedges g = 0.55, 95% CI: 0.37–0.73).…”

What influences graduate medical students’ beliefs of lower back pain? A mixed methods cross sectional study

“…This diagnostic model theorises a disease presentation as having biological, psychological and social components, therefore capturing the multiple interactions leading to LBP [7,8]. For example, the relations of local anatomical and nervous system changes, psychological factors such as anxiety and kinesiophobia, in addition to social aspects such as work absence [8][9][10][11]. Due to its complexity, management of LBP can be difficult; treatments such as analgesia, exercise and manual therapy have modest treatment effects [12].…”