Features and methods to discriminate between mechanism-based categories of pain experienced in the musculoskeletal system: a Delphi expert consensus study.

ABSTRACT: Classification of musculoskeletal pain based on underlying pain mechanisms (nociceptive, neuropathic, and nociplastic pain) is challenging. In the absence of a gold standard, verification of features that could aid in discrimination between these mechanisms in clinical practice and research depends on expert consensus. This Delphi expert consensus study aimed to: (1) identify features and assessment findings that are unique to a pain mechanism category or shared between no more than 2 categories and (2) develop a ranked list of candidate features that could potentially discriminate between pain mechanisms. A group of international experts were recruited based on their expertise in the field of pain. The Delphi process involved 2 rounds: round 1 assessed expert opinion on features that are unique to a pain mechanism category or shared between 2 (based on a 40% agreement threshold); and round 2 reviewed features that failed to reach consensus, evaluated additional features, and considered wording changes. Forty-nine international experts representing a wide range of disciplines participated. Consensus was reached for 196 of 292 features presented to the panel (clinical examination-134 features, quantitative sensory testing-34, imaging and diagnostic testing-14, and pain-type questionnaires-14). From the 196 features, consensus was reached for 76 features as unique to nociceptive (17), neuropathic (37), or nociplastic (22) pain mechanisms and 120 features as shared between pairs of pain mechanism categories (78 for neuropathic and nociplastic pain). This consensus study generated a list of potential candidate features that are likely to aid in discrimination between types of musculoskeletal pain.

Validation of a pain mechanism classification system (PMCS) in physical therapy practice.

The objective of this study was to validate the clinical application of a pain mechanism classification system (PMCS) in clinical practice. We analyzed data abstracted from the medical records of patients who were treated in the outpatient clinics of a large urban rehabilitation hospital in Chicago. We hypothesized that there would be good agreement between the PMCS determined by trained therapists and the PMCS category assigned based on a computer-generated statistical model using patients' signs and symptoms. Using cluster analysis, when we assumed five groups, 97% of patients could be classified. Sensitivity and specificity results with 95% confidence intervals were calculated for the categories using the physical therapist assigned categories (PMCS) as the criterion standard. Sensitivity for four of the five categories (inflammatory, ischemia, peripheral neurogenic, and other ranged from 72·0 to 83·1%). For the central mechanism, sensitivity was much lower at 15%. Specificity for the five categories ranged from 72·4% (ischemia) to 98·8% (central). This study provides empirical support for recent findings in the literature that the peripheral components of a PMCS can be implemented consistently in an outpatient pain clinical practice.