Mechanisms-based classifications of musculoskeletal pain: part 2 of 3: symptoms and signs of peripheral neuropathic pain in patients with low back (± leg) pain.
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Affiliation
Physiotherapy Department, St Vincent's University Hospital, Elm Park, Dublin 4, Ireland. k.smart@svuh.ieIssue Date
2012-08MeSH
AdultAged
Aged, 80 and over
Analysis of Variance
Bayes Theorem
Cluster Analysis
Cross-Sectional Studies
Female
Humans
Ireland
Low Back Pain
Male
Middle Aged
Musculoskeletal Pain
Nociceptive Pain
Peripheral Nervous System Diseases
Sensitivity and Specificity
Severity of Illness Index
Young Adult
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Mechanisms-based classifications of musculoskeletal pain: part 2 of 3: symptoms and signs of peripheral neuropathic pain in patients with low back (± leg) pain. 2012, 17 (4):345-51 Man TherJournal
Manual therapyDOI
10.1016/j.math.2012.03.003PubMed ID
22465002Abstract
As a mechanisms-based classification of pain 'peripheral neuropathic pain' (PNP) refers to pain arising from a primary lesion or dysfunction in the peripheral nervous system. Symptoms and signs associated with an assumed dominance of PNP in patients attending for physiotherapy have not been extensively studied. The purpose of this study was to identify symptoms and signs associated with a clinical classification of PNP in patients with low back (± leg) pain. Using a cross-sectional, between-subjects design; four hundred and sixty-four patients with low back (± leg) pain were assessed using a standardised assessment protocol. Patients' 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 two symptoms and one sign predictive of PNP, including: 'Pain referred in a dermatomal or cutaneous distribution', 'History of nerve injury, pathology or mechanical compromise' and 'Pain/symptom provocation with mechanical/movement tests (e.g. Active/Passive, Neurodynamic) that move/load/compress neural tissue'. This cluster was found to have high levels of classification accuracy (sensitivity 86.3%, 95% CI: 78.0-92.3; specificity 96.0%, 95% CI: 93.4-97.8; diagnostic odds ratio 150.9, 95% CI: 69.4-328.1). Pattern recognition of this empirically-derived cluster of symptoms and signs may help clinicians identify an assumed dominance of PNP mechanisms in patients with low back pain disorders in a way that might usefully inform subsequent patient management.Item Type
ArticleLanguage
enISSN
1532-2769ae974a485f413a2113503eed53cd6c53
10.1016/j.math.2012.03.003
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