PDF(Pubmed)
Abstract：
Introduction: Quantitative sensory testing is frequently used in research to assess endogenous pain modulation mechanisms, such as Temporal Summation (TS) and Conditioned Pain Modulation (CPM), reflecting excitatory and inhibitory mechanisms, respectively. Numerous studies found that a dysregulation of these mechanisms is associated with chronic pain conditions. In turn, such a patient\'s \"profile\" (increased TS and/or weakened CPM) could be used to recommend different pharmacological treatments. However, the procedure to evaluate these mechanisms is time-consuming and requires expensive equipment that is not available in the clinical setting. In this study, we aim to identify psychological, physiological and socio-demographic markers that could serve as proxies to allow healthcare professionals to identify these pain phenotypes in clinic, and consequently optimize pharmacological treatments. Method: We aim to recruit a healthy participant cohort (n = 360) and a chronic pain patient cohort (n = 108). Independent variables will include psychological questionnaires, pain measurements, physiological measures and sociodemographic characteristics. Dependent variables will include TS and CPM, which will be measured using quantitative sensory testing in a single session. We will evaluate one prediction model and two validation models (for healthy and chronic pain participants) using multiple regression analysis between TS/CPM and our independent variables. The significance thresholds will be set at p = 0.05, respectively. Perspectives: This study will allow us to develop a predictive model to compute the pain modulation profile of individual patients based on their biopsychosocial characteristics. The development of the predictive model is the first step toward the overarching goal of providing clinicians with a set of quick and cheap tests, easily applicable in clinical practice to orient pharmacological treatments.
摘要：
简介：定量感官测试经常用于评估内源性疼痛调节机制的研究。如时间求和(TS)和条件性疼痛调节(CPM)，反映兴奋和抑制机制，分别。许多研究发现，这些机制的失调与慢性疼痛有关。反过来,这种患者的“特征”(TS增加和/或CPM减弱)可用于推荐不同的药物治疗。然而,评估这些机制的过程非常耗时,并且需要昂贵的设备,而这些设备在临床中是无法获得的.在这项研究中,我们的目标是识别心理，生理和社会人口统计学标记，可以作为代理，让医疗保健专业人员在临床上识别这些疼痛表型，并因此优化药物治疗。方法：我们的目标是招募健康参与者队列（n=360）和慢性疼痛患者队列（n=108）。自变量将包括心理问卷，疼痛测量，生理指标和社会人口统计学特征。因变量将包括TS和CPM，这将在单个会话中使用定量感官测试进行测量。我们将使用TS/CPM和我们的自变量之间的多元回归分析来评估一个预测模型和两个验证模型（对于健康和慢性疼痛参与者）。显著性阈值将分别设置为p=0.05。观点：这项研究将使我们能够开发一种预测模型，以根据个体患者的生物心理社会特征来计算其疼痛调节状况。预测模型的开发是朝着为临床医生提供一套快速而廉价的测试这一总体目标迈出的第一步，易于在临床实践中应用，以指导药物治疗。
