Cluster analysis of an international Pressure Pain Threshold database identifies 4 meaningful subgroups of adults with mechanical neck pain
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Objective: To determine pressure pain detection threshold (PPDT) related phenotypes of individuals with mechanical neck pain that may be identifiable in clinical practice. Methods: This report describes a secondary analysis of 5 independent, international mechanical neck pain databases of PPDT values taken at both a local and distal region (total N=1176). Minor systematic differences in mean PPDT values across cohorts necessitated z-transformation before analysis, and each cohort was split into male and female sexes. Latent profile analysis (LPA) using the k-means approach was undertaken to identify the most parsimonious set of PPDT-based phenotypes that were both statistically and clinically meaningful. Results: LPA revealed 4 distinct clusters named according to PPDT levels at the local and distal zones: low-low PPDT (67%), mod-mod (25%), mod-high (4%), and high-high (4%). Secondary predictor variables were evaluated for intracluster and cross-cluster significance. Low-low cluster was most affected, as indicated by pain intensity, disability, and catastrophization scores all significantly above the cohort-specific and sex-specific mean, and active range of motion scores significantly below the mean. Discussion: The results suggest that there are a large proportion of people with neck pain that present with signs indicating dysfunction beyond the local tissues. Ongoing exploration of these presentations may lead to more informed management and improved outcomes.
Clinical Journal of Pain
© 2016 LWW. This is a non-final version of an article published in final form in Clinical Journal of Pain Volume 33 - Issue 5 - p 422–428, 2016. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal link for access to the definitive, published version.
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Clinical Sciences not elsewhere classified