AN EXPLORATORY DATA-DRIVEN APPROACH TO CLASSIFY SUBGROUPS OF PATIENTS WITH TEMPOROMANDIBULAR DISORDERS BASED ON PAIN MECHANISMS.

IF 4 2区 医学 Q1 CLINICAL NEUROLOGY
Giacomo Asquini, Valter Devecchi, Domenico Viscuso, Rosaria Bucci, Ambra Michelotti, Bernard Xw Liew, Deborah Falla
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引用次数: 0

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 TMD, 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.

一种探索性的数据驱动方法,根据疼痛机制对颞下颌关节紊乱症患者进行分组。
颞下颌关节紊乱症(TMD)是一种常见的肌肉骨骼疾病,由于其非特异性,给治疗带来了挑战。根据神经生物学疼痛机制对 TMDs 患者进行分类,可为有针对性的治疗提供一种可行的方法。本观察性研究(1)采用网络分析(NA),通过调查生物心理社会变量之间的关系来探索 TMD 的复杂性;(2)根据特定机制的疼痛类别验证潜在的 TMD 亚组。研究共纳入 112 名 TMD 患者。生物心理社会变量包括:一般健康状况、心理社会特征、TMD 疼痛和 TMD 特征。NA评估了变量之间的关联,并确定了每个特征在网络中的作用。分层聚类用于确定 TMD 亚组。NA显示,主要在相同特征域内存在明显的相关性,表明症状与心理因素之间存在强烈的相互作用。聚类分析确定了由痛觉机制和非痛觉机制驱动的两个亚组;非痛觉机制组表现出更高程度的焦虑、抑郁、疼痛灾难化、中枢敏感化、疼痛持续时间和更多疼痛部位,同时睡眠质量、生活质量和健康状况也较差。相比之下,痛觉组的最大张口度(MMO)受限,颞下颌关节触诊和张口时疼痛加剧,对手法治疗的反应更积极。在所有特征中,心理因素、疼痛部位和最大张口度是区分亚组的主要原因。通过采用数据驱动的方法,这些结果支持了考虑疼痛的神经生物学基础对改进患者分类的重要作用。这些知识有助于临床推理和个性化治疗。观点:本研究采用网络分析方法探索了 TMD 患者复杂的生物-心理-社会相互作用,确定了中枢敏感性量表和无痛最大张口度等重要变量。研究结果区分了潜在的痛觉性和非痛觉性疼痛亚群,为制定有针对性的治疗策略提供了重要依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Pain
Journal of Pain 医学-临床神经学
CiteScore
6.30
自引率
7.50%
发文量
441
审稿时长
42 days
期刊介绍: The Journal of Pain publishes original articles related to all aspects of pain, including clinical and basic research, patient care, education, and health policy. Articles selected for publication in the Journal are most commonly reports of original clinical research or reports of original basic research. In addition, invited critical reviews, including meta analyses of drugs for pain management, invited commentaries on reviews, and exceptional case studies are published in the Journal. The mission of the Journal is to improve the care of patients in pain by providing a forum for clinical researchers, basic scientists, clinicians, and other health professionals to publish original research.
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