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用多模态疼痛体图量化疼痛位置和强度。

Quantifying Pain Location and Intensity with Multimodal Pain Body Diagrams.

机构信息

Anesthesiology (Division of Pain Management), University of California, San Francisco.

Neurological Surgery and UCSF Weill Institute of Neurosciences, University of California, San Francisco.

出版信息

J Vis Exp. 2023 Jul 7(197). doi: 10.3791/65334.

Abstract

To quantify an individual's subjective pain severity, standardized pain rating scales such as the numeric rating scale (NRS), visual analog scale (VAS), or McGill pain questionnaire (MPQ) are commonly used to assess pain on a numerical scale. However, these scales are often biased and fail to capture the complexity of pain experiences. In contrast, clinical practice often requires patients to report areas of pain by drawing on a body diagram, which is an effective but qualitative tool. The method presented here extracts quantifiable metrics from pain body diagrams (PBDs) which are validated against the NRS, VAS, and MPQ pain scales. By using a novel pressure-hue transformation on a digital tablet, different drawing pressures applied with a digital stylus can be represented as different hues on a PBD. This produces a visually intuitive diagram of hues ranging from green to blue to red, representing mild to moderate to most painful regions, respectively. To quantify each PBD, novel pain metrics were defined: (1) PBD mean intensity, which equals the sum of each pixel's hue value divided by the number of colored pixels, (2) PBD coverage, which equals the number of colored pixels divided by the total number of pixels on the body, and (3) PBD sum intensity, which equals the sum of all pixels' hue values. Using correlation and information theory analyses, these PBD metrics were shown to have high concordance with standardized pain metrics, including NRS, VAS and MPQ. In conclusion, PBDs can provide novel spatial and quantitative information that can be repeatedly measured and tracked over time to comprehensively characterize a participant's pain experience.

摘要

为了量化个体的主观疼痛严重程度,通常使用标准化疼痛评分量表,如数字评分量表(NRS)、视觉模拟量表(VAS)或麦吉尔疼痛问卷(MPQ),在数字量表上评估疼痛。然而,这些量表往往存在偏差,无法捕捉疼痛体验的复杂性。相比之下,临床实践通常要求患者通过在身体图表上绘制来报告疼痛区域,这是一种有效但定性的工具。这里提出的方法从疼痛体图(PBD)中提取可量化的指标,这些指标经过 NRS、VAS 和 MPQ 疼痛量表的验证。通过在数字平板电脑上使用新颖的压力-色调转换,用数字手写笔施加的不同绘图压力可以表示为 PBD 上的不同色调。这产生了一个从绿色到蓝色到红色的色调直观图,分别代表轻度到中度到最疼痛的区域。为了量化每个 PBD,定义了新的疼痛指标:(1)PBD 平均强度,等于每个像素的色调值之和除以有色像素的数量,(2)PBD 覆盖范围,等于有色像素的数量除以身体上总像素的数量,(3)PBD 总和强度,等于所有像素色调值的总和。通过相关性和信息理论分析,这些 PBD 指标与标准化疼痛指标(包括 NRS、VAS 和 MPQ)具有高度一致性。总之,PBD 可以提供新颖的空间和定量信息,可以重复测量并随时间跟踪,以全面描述参与者的疼痛体验。

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