Hierarchical clustering by patient-reported pain distribution alone identifies distinct chronic pain subgroups differing by pain intensity, quality, and clinical outcomes.
Hierarchical clustering by patient-reported pain distribution alone identifies distinct chronic pain subgroups differing by pain intensity, quality, and clinical outcomes.
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DOI：
10.1371/journal.pone.0254862
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Wasan AD
中科院分区：
文献类型：
--
作者：
Alter BJ;Anderson NP;Gillman AG;Yin Q;Jeong JH;Wasan AD
在临床实践中，慢性疼痛的身体分布通常与其他体征和症状结合使用，以支持诊断或治疗计划。例如，纤维肌痛的诊断涉及使用绘制的身体地图计算患者报告的疼痛区域。目前尚不清楚疼痛分布模式是否独立地告知疼痛体验的各个方面并影响患者的结局。本研究的目的是使用与诊断或患者报告的疼痛经历方面无关的算法方法评价疼痛分布模式的临床相关性。一个大型患者队列（N = 21，658）完成了疼痛身体地图和多维疼痛评估。使用层次聚类的患者单独的身体地图选择，九个不同的亚组出现了不同的模式的身体区域选择。临床医生对群集身体图的审查概括了一些临床相关的疼痛分布模式，如膝以下放射性腰痛和广泛疼痛，以及一些独特的模式。人口统计学和医学特征、疼痛强度、疼痛影响和神经性疼痛质量在群集亚组之间均存在显著差异。多变量模型表明，集群成员独立预测疼痛强度和神经病理性疼痛的质量。在完成3个月随访问卷调查的患者子集（N = 7，138）中，聚类成员独立预测疼痛改善的可能性，身体功能，以及与多学科疼痛护理相关的积极的总体变化印象。这项研究报告了一种新的方法分组患者的疼痛分布使用算法的方法。疼痛分布亚组与疼痛强度、影响和临床相关结局的差异显著相关。在未来，通过疼痛分布进行算法聚类可能是为疼痛管理的个性化而开发的慢性疼痛生物特征的一个重要方面。
In clinical practice, the bodily distribution of chronic pain is often used in conjunction with other signs and symptoms to support a diagnosis or treatment plan. For example, the diagnosis of fibromyalgia involves tallying the areas of pain that a patient reports using a drawn body map. It remains unclear whether patterns of pain distribution independently inform aspects of the pain experience and influence patient outcomes. The objective of the current study was to evaluate the clinical relevance of patterns of pain distribution using an algorithmic approach agnostic to diagnosis or patient-reported facets of the pain experience. A large cohort of patients (N = 21,658) completed pain body maps and a multi-dimensional pain assessment. Using hierarchical clustering of patients by body map selection alone, nine distinct subgroups emerged with different patterns of body region selection. Clinician review of cluster body maps recapitulated some clinically-relevant patterns of pain distribution, such as low back pain with radiation below the knee and widespread pain, as well as some unique patterns. Demographic and medical characteristics, pain intensity, pain impact, and neuropathic pain quality all varied significantly across cluster subgroups. Multivariate modeling demonstrated that cluster membership independently predicted pain intensity and neuropathic pain quality. In a subset of patients who completed 3-month follow-up questionnaires (N = 7,138), cluster membership independently predicted the likelihood of improvement in pain, physical function, and a positive overall impression of change related to multidisciplinary pain care. This study reports a novel method of grouping patients by pain distribution using an algorithmic approach. Pain distribution subgroup was significantly associated with differences in pain intensity, impact, and clinically relevant outcomes. In the future, algorithmic clustering by pain distribution may be an important facet in chronic pain biosignatures developed for the personalization of pain management.
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