Chronic pain patients' treatment preferences: a discrete-choice experiment
Chronic pain patients' treatment preferences: a discrete-choice experiment
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DOI：
10.1007/s10198-014-0614-4
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发表时间：
2015-07-01
影响因子：
4.4
通讯作者：
Nuebling, Matthias
中科院分区：
文献类型：
--
作者：
Muehlbacher, Axel C.;Junker, Uwe;Nuebling, Matthias
本研究的目的是从慢性疼痛患者的角度识别、记录和衡量疼痛药物的相关属性。在患有“慢性神经性疼痛”的患者亚群中，对三组患者进行了深入分析：神经性背痛患者、疼痛性糖尿病多发性神经病患者和患有疱疹后神经痛所致疼痛的患者。核心问题是：“患者对止痛药的评估基于哪些特征，哪些特征在评估和选择可能的治疗方法时最有用？“进行了详细的文献综述，患者焦点小组以及与广泛认可的疼痛治疗专家的面对面访谈，以确定疼痛药物的相关治疗属性。进行了预测试，以验证结构的相关和主导属性，使用因素分析，通过评估最常提到的代表每个因素。离散选择实验（DCE）使用了一项基于自我报告的患者数据的调查，包括社会人口统计学和有关疼痛治疗的特定参数。此外，根据患者在painDETECT（A（R））问卷中的评分，确定所有患者的神经性疼痛成分。对于DCE的统计数据分析，使用了随机效应logit模型并给出了系数。共有1，324名德国患者参与了调查，其中44%患有神经性背痛（包括混合性疼痛综合征），10%抱怨糖尿病性多发性神经病，4%报告了疱疹后神经痛引起的疼痛。共36个单一的质量方面的疼痛治疗，在定性调查中发现，分为7个维度的因素分析。这7个维度用作DCE的属性。DCE模型对治疗决策的相关属性进行了以下排序：“无性格变化”、“恶心和呕吐较少”、“疼痛减轻”（系数：所有属性> 0.9，“高影响”），“快速效应”，“低成瘾风险”（与0.5相似的系数，“中等影响”）、“具有共患病的适用性”（与0.3相似的系数）和“睡眠质量的改善”（与0.25相似的系数）。所有属性均具有高度显著性（p < 0.001）。结果旨在使早期选择个性化止痛药成为可能。研究结果表明，DCE是一种适当的手段，用于识别患者的偏好时，正在与特定的止痛药治疗。由于疼痛感知本质上是主观的，因此识别患者A的偏好将使治疗师能够更好地开发和实施以患者为导向的慢性疼痛治疗。因此，必须提高治疗师对患者偏好的理解，以便做出有关疼痛治疗的决定。DCE和直接评估应该成为有效的工具，以引起慢性疼痛的治疗偏好。
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(A (R)) 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 similar to 0.5, "middle impact"), "applicability with comorbidity" (coefficient similar to 0.3), and "improvement of quality of sleep" (coefficient similar to 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 patientsA ' preferences will enable therapists to better develop and implement patient-oriented treatment of chronic pain. It is therefore essential to improve the therapistsA ' 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.