IGM Institute Health Economics and Healthcare Management, Hochschule Neubrandenburg, Brodaer Straße 2, 17033, Neubrandenburg, Germany,
Eur J Health Econ. 2015 Jul;16(6):613-28. doi: 10.1007/s10198-014-0614-4. Epub 2014 Jun 21.
The objective of this study was to identify, document, and weight attributes of a pain medication that are relevant from the perspective of patients with chronic pain. Within the sub-population of patients suffering from "chronic neuropathic pain", three groups were analyzed in depth: patients with neuropathic back pain, patients with painful diabetic polyneuropathy, and patients suffering from pain due to post-herpetic neuralgia. The central question was: "On which features do patients base their assessment of pain medications and which features are most useful in the process of evaluating and selecting possible therapies?"
A detailed literature review, focus groups with patients, and face-to-face interviews with widely recognized experts for pain treatment were conducted to identify relevant treatment attributes of a pain medication. A pre-test was conducted to verify the structure of relevant and dominant attributes using factor analyses by evaluating the most frequently mentioned representatives of each factor. The Discrete-Choice Experiment (DCE) used a survey based on self-reported patient data including socio-demographics and specific parameters concerning pain treatment. Furthermore, the neuropathic pain component was determined in all patients based on their scoring in the painDETECT(®) questionnaire. For statistical data analysis of the DCE, a random effect logit model was used and coefficients were presented.
A total of 1,324 German patients participated in the survey, of whom 44 % suffered from neuropathic back pain (including mixed pain syndrome), 10 % complained about diabetic polyneuropathy, and 4 % reported pain due to post-herpetic neuralgia. A total of 36 single quality aspects of pain treatment, detected in the qualitative survey, were grouped in 7 dimensions by factor analysis. These 7 dimensions were used as attributes for the DCE. The DCE model resulted in the following ranking of relevant attributes for treatment decision: "no character change", "less nausea and vomiting", "pain reduction" (coefficient: >0.9 for all attributes, "high impact"), "rapid effect", "low risk of addiction" (coefficient ~0.5, "middle impact"), "applicability with comorbidity" (coefficient ~0.3), and "improvement of quality of sleep" (coefficient ~0.25). All attributes were highly significant (p < 0.001).
The results were intended to enable early selection of an individualized pain medication. The results of the study showed that DCE is an appropriate means for the identification of patient preferences when being treated with specific pain medications. Due to the fact that pain perception is subjective in nature, the identification of patients´ preferences will enable therapists to better develop and implement patient-oriented treatment of chronic pain. It is therefore essential to improve the therapists´ understanding of patient preferences in order to make decisions concerning pain treatment. DCE and direct assessment should become valid instruments to elicit treatment preferences in chronic pain.
本研究旨在从慢性疼痛患者的角度确定、记录和权衡疼痛药物的相关属性。在患有“慢性神经性疼痛”的亚人群中，深入分析了三组：神经性背痛患者、患有痛性糖尿病多发性神经病的患者和患有疱疹后神经痛的患者。核心问题是：“患者基于哪些特征来评估疼痛药物，哪些特征在评估和选择可能的治疗方法过程中最有用？”
通过详细的文献回顾、患者焦点小组和广泛认可的疼痛治疗专家的面对面访谈，确定疼痛药物的相关治疗属性。通过评估每个因素中最常提到的代表来进行因子分析，进行预测试以验证相关和主导属性的结构。离散选择实验（DCE）使用基于自我报告的患者数据的调查，包括社会人口统计学和特定的疼痛治疗参数。此外，所有患者的神经性疼痛成分均根据其在疼痛 DETECT（®）问卷中的评分确定。对于 DCE 的统计数据分析，使用随机效应对数模型并呈现系数。
共有 1324 名德国患者参与了调查，其中 44%患有神经性背痛（包括混合疼痛综合征），10%抱怨患有糖尿病多发性神经病，4%报告患有疱疹后神经痛。通过定性调查检测到的 36 个疼痛治疗单一质量方面通过因子分析分为 7 个维度。这 7 个维度被用作 DCE 的属性。DCE 模型得出了以下用于治疗决策的相关属性排序：“无性格变化”、“恶心和呕吐较少”、“疼痛减轻”（系数：所有属性均>0.9，“高影响”）、“快速起效”、“低成瘾风险”（系数0.5，“中影响”）、“伴有合并症的适用性”（系数0.3）和“改善睡眠质量”（系数~0.25）。所有属性均具有高度显著性（p<0.001）。
这些结果旨在实现早期选择个体化疼痛药物。研究结果表明，DCE 是确定接受特定疼痛药物治疗的患者偏好的适当手段。由于疼痛感知本质上是主观的，因此确定患者的偏好将使治疗师能够更好地为慢性疼痛患者制定和实施以患者为中心的治疗方法。因此，提高治疗师对患者偏好的理解对于做出疼痛治疗决策至关重要。DCE 和直接评估应成为引出慢性疼痛治疗偏好的有效工具。
