The Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel Institute of Pain Medicine, Rambam Health Care Campus, Haifa, Israel Medasense Biometrics Ltd., Ofakim, Israel Pain Management Center, Sheba Medical Center, Tel Hashomer, Israel.
Pain. 2012 Sep;153(9):1807-1814. doi: 10.1016/j.pain.2012.04.008. Epub 2012 May 29.
Although it is well known that pain induces changes in autonomic parameters, the extent to which these changes correlate with the experience of pain is under debate. The aim of the present study was to compare a combination of multiple autonomic parameters and each parameter alone in their ability to differentiate among 4 categories of pain intensity. Tonic heat stimuli (1minute) were individually adjusted to induce no pain, low, medium, and high pain in 45 healthy volunteers. Electrocardiogram, photoplethysmogram, and galvanic skin response were recorded, and the following parameters were calculated: heart rate; heart rate variability-high frequency (0.15 to 0.4Hz) spectral power; skin conductance level; number of skin conduction fluctuations; and photoplethysmographic pulse wave amplitude. A combination of parameters was created by fitting an ordinal cumulative logit model to the data and using linear coefficients of the model. Friedman test with post-hoc Wilcoxon test were used to compare between pain intensity categories for every parameter alone and for their linear combination. All of the parameters successfully differentiated between no pain and all other pain categories. However, none of the parameters differentiated between all 3 pain categories (i.e., low and medium; medium and high; low and high). In contrast, the linear combination of parameters significantly differentiated not only between pain and no pain, but also between all pain categories (P<.001 to .02). These results suggest that multiparameter approaches should be further investigated to make progress toward reliable autonomic-based pain assessment.
尽管众所周知疼痛会引起自主参数的变化，但这些变化与疼痛体验的相关性仍存在争议。本研究旨在比较多种自主参数组合和各参数单独区分 4 种疼痛强度类别的能力。在 45 名健康志愿者中，分别调整连续热刺激（1 分钟）以引起无痛、轻度、中度和重度疼痛。记录心电图、光体积描记图和皮肤电反应，并计算以下参数：心率；心率变异性高频（0.15 至 0.4Hz）谱功率；皮肤电导水平；皮肤传导波动次数；和光体积描记脉搏波幅度。通过将有序累积对数模型拟合到数据中，并使用模型的线性系数，创建参数组合。对于每个参数单独和其线性组合，使用 Friedman 检验和事后 Wilcoxon 检验比较疼痛强度类别之间的差异。所有参数都成功地区分了无痛和其他所有疼痛类别。然而，没有任何参数可以区分所有 3 种疼痛类别（即轻度和中度；中度和重度；轻度和重度）。相比之下，参数的线性组合不仅显著区分了疼痛和无痛，而且还区分了所有疼痛类别（P<.001 至.02）。这些结果表明，应进一步研究多参数方法，以实现可靠的基于自主的疼痛评估。
