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An exploratory data-driven approach to classify subgroups of patients with temporomandibular disorders based on pain mechanisms. | LitMetric

An exploratory data-driven approach to classify subgroups of patients with temporomandibular disorders based on pain mechanisms.

J Pain

Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom. Electronic address:

Published: October 2024

AI Article Synopsis

  • Temporomandibular disorders (TMDs) pose treatment challenges due to their complex, non-specific symptoms and are influenced by a mix of biological, psychological, and social factors.
  • An observational study with 102 TMD patients employed network analysis to explore these factors and identify potential subgroups based on pain mechanisms.
  • Results showed two distinct groups: one with nociceptive pain (better responses to manual therapy) and another with nociplastic pain (higher anxiety, depression, and poorer overall health), highlighting the importance of understanding pain mechanisms for personalized treatment approaches.

Article Abstract

Temporomandibular disorders (TMDs) are a common musculoskeletal condition, presenting treatment challenges due to their non-specific nature. Categorizing patients with TMDs into clusters based on neurobiological pain mechanisms could provide a promising approach to facilitate targeted treatments. This observational study (1) used a network analysis (NA) to explore the complexity of TMDs by investigating relationships among biopsychosocial variables, and (2) validated potential TMD subgroups based on mechanism-specific pain categories. One hundred and two patients with TMD were included. Biopsychosocial variables covered: general health, psychosocial features, TMD pain, and TMD characteristics. A NA evaluated the associations between variables and determined the role of each feature within the network. Hierarchical clustering was used to identify TMD subgroups. The NA revealed significant correlations primarily within the same feature domains, indicating a strong interplay between symptoms and psychological factors. Cluster analysis identified two subgroups driven by nociceptive and nociplastic pain mechanisms; the nociplastic group exhibited higher levels of anxiety, depression, pain catastrophization, central sensitization, pain duration, and more pain locations, along with poorer sleep quality, quality of life, and health status. In contrast, the nociceptive group exhibited restricted maximal mouth opening (MMO), heightened pain during TMJ palpation and mouth opening, and a greater positive response to manual therapy. Across all features, psychological factors, pain locations, and MMO primarily contributed to the separation of subgroups. By adopting a data-driven approach, these results support the significant role of considering the neurobiological basis of pain to improve patient classification. This knowledge may facilitate clinical reasoning and personalized treatments. PERSPECTIVE: This study used a network analysis to explore the complex biopsychosocial interactions present in people with TMDs, identifying important variables such as the Central Sensitization Inventory and pain-free maximal mouth opening. The findings distinguish potential nociceptive and nociplastic pain subgroups, offering important insights for targeted therapeutic strategies.

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Source
http://dx.doi.org/10.1016/j.jpain.2024.104721DOI Listing

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