Reimer Maren, Forstenpointner Julia, Hartmann Alina, Otto Jan Carl, Vollert Jan, Gierthmühlen Janne, Klein Thomas, Hüllemann Philipp, Baron Ralf
Division of Neurological Pain Research and Therapy, University Hospital Schleswig- Holstein, Campus Kiel, Germany.
Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom.
Pain Rep. 2020 May 21;5(3):e820. doi: 10.1097/PR9.0000000000000820. eCollection 2020 May-Jun.
Stratification of patients according to the individual sensory phenotype has been suggested a promising method to identify responders for pain treatment. However, many state-of-the-art sensory testing procedures are expensive or time-consuming.
Therefore, this study aimed to present a selection of easy-to-use bedside devices.
In total, 73 patients (39 m/34 f) and 20 controls (11 m/9 f) received a standardized laboratory quantitative sensory testing (QST) and a bedside-QST. In addition, 50 patients were tested by a group of nonexperienced investigators to address the impact of training. The sensitivity, specificity, and receiver-operating characteristics were analyzed for each bedside-QST parameter as compared to laboratory QST. Furthermore, the patients' individual sensory phenotype (ie, cluster) was determined using laboratory QST, to select bedside-QST parameters most indicative for a correct cluster allocation.
The bedside-QST parameters "loss of cold perception to 22°C metal," "hypersensitivity towards 45°C metal," "loss of tactile perception to Q-tip and 0.7 mm CMS hair," as well as "the allodynia sum score" indicated good sensitivity and specificity (ie, 70%). Results of interrater variability indicated that training is necessary for individual parameters (ie, CMS 0.7). For the cluster assessment, the respective bedside quantitative sensory testing (QST) parameter combination indicated the following agreements as compared to laboratory QST stratification: excellent for "sensory loss" (area under the curve [AUC] = 0.91), good for "thermal hyperalgesia" (AUC = 0.83), and fair for "mechanical hyperalgesia" (AUC = 0.75).
This study presents a selection of bedside parameters to identify the individual sensory phenotype as cost and time efficient as possible.
根据个体感觉表型对患者进行分层被认为是一种识别疼痛治疗反应者的有前景的方法。然而，许多先进的感觉测试程序昂贵或耗时。
因此，本研究旨在介绍一些易于使用的床边设备。
总共73例患者（39例男性/34例女性）和20名对照者（11例男性/9例女性）接受了标准化实验室定量感觉测试（QST）和床边QST。此外，50例患者由一组无经验的研究人员进行测试，以探讨培训的影响。将每个床边QST参数与实验室QST进行比较，分析其敏感性、特异性和受试者工作特征。此外，使用实验室QST确定患者的个体感觉表型（即聚类），以选择最能指示正确聚类分配的床边QST参数。
床边QST参数“对22°C金属冷觉丧失”、“对45°C金属超敏反应”、“对棉签和0.7 mm CMS毛发触觉丧失”以及“异常性疼痛总分”显示出良好的敏感性和特异性（即70%）。评分者间变异性结果表明，个别参数（即CMS 0.7）需要培训。对于聚类评估，与实验室QST分层相比，相应的床边定量感觉测试（QST）参数组合显示出以下一致性：“感觉丧失”为优秀（曲线下面积[AUC]=0.91），“热痛觉过敏”为良好（AUC=0.83），“机械性痛觉过敏”为中等（AUC=0.75）。
本研究介绍了一系列床边参数，以尽可能经济高效地识别个体感觉表型。
