The International Association for the Study of Pain defines three pain types presumed to involve different mechanisms - nociceptive, neuropathic and nociplastic. Basis on the hypothesis that these pain types should guide matching of patients with treatments, work has been undertaken to identify features to discriminate between them for clinical use. This study aimed to evaluate the validity of these features to discriminate between pain types. Subjective and physical features were evaluated in a cohort of 350 individuals with chronic musculoskeletal pain attending a chronic pain management program. Analysis tested the hypothesis that, if the features nominated for each pain type represent 3 different groups, then (i) cluster analysis should identify 3 main clusters of patients, (ii) these clusters should align with the pain type allocated by an experienced clinician, (iii) patients within a cluster should have high expression of the candidate features proposed to assist identification of that pain type. Supervised machine learning interrogated features with the greatest and least importance for discrimination; and probabilistic analysis probed the potential for coexistence of multiple pain types. Results confirmed that data could be best explained by 3 clusters, clusters were characterized by a priori specified features, and agreed with the designation of the experienced clinical with 82% accuracy. Supervised analysis highlighted features that contributed most and least to the classification to pain type and probabilistic analysis reinforced the presence of mixed pain types. These findings support the foundation for further refinement of a clinical tool to discriminate between pain types.

The authors have declared no competing interest.

This study was funded by NHMRC Grants #2027473 and #1194937.

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Ethics committee/IRB of La Trobe University gave ethical approval for this work

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