Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
J Pain. 2010 Dec;11(12):1394-402. doi: 10.1016/j.jpain.2010.03.014. Epub 2010 Jun 8.
Although large interindividual differences in pain exist, the underlying factors that contribute to these variations remain poorly understood. Consequently, being able to accurately explain variability in pain ratings in terms of its contributing factors could provide insights into developing a better understanding of individual differences in pain experience. In the present investigation, we show that a significant portion of the variability in experimental heat pain ratings may be predicted using simple quantitative sensory testing and a series of psychological questionnaires including State Trait and Anxiety Inventory (STAI), Center for Epidemiologic Studies - Depression Scale (CES-D), and Positive and Negative Affect Schedule - Expanded form (PANAS-X). A factor analysis was used to reduce individual predictors into sets of composite predictive factors. A multifactorial model that was generated from these factors can reliably predict a significant amount of the variability in heat pain sensitivity ratings (r² = .537, P = .027). Moreover, individual variables including heat pain thresholds and self-assessment of pain sensitivity were found to be poor predictors of heat pain sensitivity. Taken together, these results suggest that a variety of factors underlie individual differences in pain experience and that a reliable model for predicting pain should be constructed from a combination of these factors.
The present study provides a way to predict subjects' experimental heat pain sensitivity using a multifactorial model generated from a combination of sensory and psychological factors. Future application of such a model in the studies of clinical pain could potentially improve the quality of care provided for patients in pain.
尽管存在个体间巨大的疼痛差异,但导致这些差异的潜在因素仍知之甚少。因此,能够根据导致疼痛的因素准确地解释疼痛评分的可变性,可能有助于更好地理解个体之间的疼痛差异。在本研究中,我们表明,使用简单的定量感觉测试和一系列心理问卷,包括状态特质焦虑量表(STAI)、流行病学研究中心抑郁量表(CES-D)和正性负性情绪量表-扩展形式(PANAS-X),可以预测实验性热痛评分的很大一部分可变性。使用因子分析将个体预测因子简化为复合预测因子组。从这些因子生成的多因素模型可以可靠地预测热痛敏感性评分的很大一部分可变性(r²=0.537,P=0.027)。此外,包括热痛阈值和自我评估疼痛敏感性在内的个体变量被发现是热痛敏感性的不良预测因子。综上所述,这些结果表明,个体疼痛体验差异的背后存在多种因素,可靠的疼痛预测模型应该由这些因素的组合构建而成。
本研究提供了一种使用基于感觉和心理因素组合的多因素模型来预测受试者实验性热痛敏感性的方法。在临床疼痛研究中应用这种模型,未来可能会提高疼痛患者的护理质量。