The clinical utility of pain classification in non-specific arm pain
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NOTICE: this is the author’s version of a work that was accepted for publication in Manual Therapy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Manual Therapy, Vol. 20 (2015). DOI: 10.1016/j.math.2014.08.010
Mechanisms-based pain classification has received considerable attention recently for its potential use in clinical decision making. A number of algorithms for pain classification have been proposed. Non-specific arm pain (NSAP) is a poorly defined condition, which could benefit from classification according to pain mechanisms to improve treatment selection. This study used three published classification algorithms (hereafter called NeuPSIG, Smart, Schafer) to investigate the frequency of different pain classifications in NSAP and the clinical utility of these systems in assessing NSAP. Forty people with NSAP underwent a clinical examination and quantitative sensory testing. Findings were used to classify participants according to three classification algorithms. Frequency of pain classification including number unclassified was analysed using descriptive statistics. Inter-rater agreement was analysed using kappa coefficients. NSAP was primarily classified as ‘unlikely neuropathic pain’ using NeuPSIG criteria, ‘peripheral neuropathic pain’ using the Smart classification and ‘peripheral nerve sensitisation’ using the Schafer algorithm. Two of the three algorithms allowed classification of all but one participant; up to 45% of participants (n = 18) were categorised as mixed by the Smart classification. Inter-rater agreement was good for the Schafer algorithm (к = 0.78) and moderate for the Smart classification (к = 0.40). A kappa value was unattainable for the NeuPSIG algorithm but agreement was high. Pain classification was achievable with high inter-rater agreement for two of the three algorithms assessed. The Smart classification may be useful but requires further direction regarding the use of clinical criteria included. The impact of adding a pain classification to clinical assessment on patient outcomes needs to be evaluated.
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