Using a cross-sectional, between-subjects design; four hundred and sixty-four patients with low back (卤leg) pain were assessed using a standardised assessment protocol. Patients鈥?pain was assigned a mechanisms-based classification based on experienced clinical judgement. Clinicians then completed a clinical criteria checklist specifying the presence or absence of various clinical criteria.
A binary logistic regression analysis with Bayesian model averaging identified a cluster of three symptoms and one sign predictive of CSP, including: 鈥?em>Disproportionate, non-mechanical, unpredictable pattern of pain provocation in response to multiple/non-specific aggravating/easing factors鈥? 鈥?em>Pain disproportionate to the nature and extent of injury or pathology鈥? 鈥?em>Strong association with maladaptive psychosocial factors (e.g. negative emotions, poor self-efficacy, maladaptive beliefs and pain behaviours)鈥?and 鈥?em>Diffuse/non-anatomic areas of pain/tenderness on palpation鈥?
This cluster was found to have high levels of classification accuracy (sensitivity 91.8%, 95%confidence interval (CI): 84.5-96.4; specificity 97.7%, 95%CI: 95.6-99.0).
Pattern recognition of this empirically-derived cluster of symptoms and signs may help clinicians identify an assumed dominance of CSP in patients with low back pain disorders in a way that might usefully inform their management.