Systematic Review and Synthesis of Mechanism-based Classification Systems for Pain Experienced in the Musculoskeletal System.

Objectives: Improvements in pain management might be achieved by matching treatment to underlying mechanisms for pain persistence. Many authors argue for a mechanism-based classification of pain, but the field is challenged by the wide variation in the proposed terminology, definitions, and typical characteristics. This study aimed to (1) systematically review mechanism-based classifications of pain experienced in the musculoskeletal system; (2) synthesize and thematically analyze classifications, using the International Association for the Study of Pain categories of nociceptive, neuropathic, and nociplastic as an initial foundation; and (3) identify convergence and divergence between categories, terminology, and descriptions of each mechanism-based pain classification.

Materials And Methods: Databases were searched for papers that discussed a mechanism-based classification of pain experienced in the musculoskeletal system. Terminology, definitions, underlying neurobiology/pathophysiology, aggravating/easing factors/response to treatment, and pain characteristics were extracted and synthesized on the basis of thematic analysis.

Results: From 224 papers, 174 terms referred to pain mechanisms categories. Data synthesis agreed with the broad classification on the basis of ongoing nociceptive input, neuropathic mechanisms, and nociplastic mechanisms (eg, central sensitization). "Mixed," "other," and the disputed categories of "sympathetic" and "psychogenic" pain were also identified. Thematic analysis revealed convergence and divergence of opinion on the definitions, underlying neurobiology, and characteristics.

Discussion: Some pain categories were defined consistently, and despite the extensive efforts to develop global consensus on pain definitions, disagreement still exists on how each could be defined, subdivided, and their characteristic features that could aid differentiation. These data form a foundation for reaching consensus on classification.