Abstract
BACKGROUND: The clinical presentation of neck-arm pain is heterogeneous with varying underlying pain types (nociceptive/neuropathic/mixed) and pain mechanisms (peripheral/central sensitization). A mechanism-based clinical framework for spinally referred pain has been proposed, which classifies into (1) somatic pain, (2) neural mechanosensitivity, (3) radicular pain, (4) radiculopathy and mixed pain presentations. This study aims to (i) investigate the application of the clinical framework in patients with neck-arm pain, (ii) determine their somatosensory, clinical and psychosocial profile and (iii) observe their clinical course over time. METHOD: We describe a study protocol. Patients with unilateral neck-arm pain (n = 180) will undergo a clinical examination, after which they will be classified into subgroups according to the proposed clinical framework. Standardized quantitative sensory testing (QST) measurements will be taken in their main pain area and contralateral side. Participants will have to complete questionnaires to assess function (Neck Disability Index), psychosocial factors (Tampa Scale of Kinesiophobia, Pain Catastrophizing Scale, Depression, anxiety and stress scale), neuropathic pain (Douleur Neuropathique 4 Questions, PainDETECT Questionnaire) and central sensitization features (Central Sensitization Inventory). Follow-ups at three, six and 12 months include the baseline questionnaires. The differences of QST data and questionnaire outcomes between and within groups will be analyzed using (M)AN(C)OVA and/or regression models. Repeated measurement analysis of variance or a linear mixed model will be used to calculate the differences between three, six, and 12 months outcomes. Multiple regression models will be used to analyze potential predictors for the clinical course. CONCLUSION: The rationale for this study is to assess the usability and utility of the proposed clinical framework as well as to identify possible differing somatosensory and psychosocial phenotypes between the subgroups. This could increase our knowledge of the underlying pain mechanisms. The longitudinal analysis may help to assess possible predictors for pain persistency.