McWilliams Lachlan A, Sarty Gordon, Kowal John, Wilson Keith G
*Department of Psychology, University of Saskatchewan, Saskatoon, Saskatchewan†Department of Psychology, The Ottawa Hospital‡Clinical Epidemiology Program, The Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
Clin J Pain. 2017 Oct;33(10):899-904. doi: 10.1097/AJP.0000000000000477.
Major depression in the context of chronic pain has been conceptualized implicitly as a latent variable, in which symptoms are viewed as manifestations of an underlying disorder. A network approach provides an alternative model and posits that symptoms are causally connected, rather than merely correlated, and that disorders exist as systems, rather than as entities. The present study applied a network analysis to self-reported symptoms of major depression in patients with chronic pain. The goals of the study were to describe the network of depressive symptoms in individuals with chronic pain and to illustrate the potential of network analysis for generating new research questions and treatment strategies.
Patients (N=216) admitted to an interdisciplinary chronic pain rehabilitation program provided symptom self-reports using the Patient Health Questionnaire-9. Well-established network analyses methods were used to illustrate the network of depressive symptoms and determine the centrality of each symptom (ie, the degree of connection with other symptoms in the network).
The most central symptoms were difficulty concentrating, loss of interest or pleasure, depressed mood, and fatigue, although the relative position of each symptom varied slightly, depending on the centrality measure considered.
Consistent with past research with patients undergoing treatment for major depression, the current findings are supportive of a model in which depressive symptoms are causally connected within a network rather than being manifestations of a common underlying disorder. The research and clinical implications of the findings, such as developing treatments targeting the most central symptoms, are discussed.
慢性疼痛背景下的重度抑郁症一直被隐含地概念化为一个潜在变量，其中症状被视为一种潜在疾病的表现。网络分析方法提供了一种替代模型，并假定症状之间存在因果联系，而非仅仅是相关关系，且疾病是以系统形式存在，而非实体形式。本研究对慢性疼痛患者自我报告的重度抑郁症状进行了网络分析。该研究的目的是描述慢性疼痛患者的抑郁症状网络，并说明网络分析在提出新研究问题和治疗策略方面的潜力。
纳入一个跨学科慢性疼痛康复项目的患者（N = 216）使用患者健康问卷-9进行症状自我报告。采用成熟的网络分析方法来说明抑郁症状网络，并确定每个症状的中心性（即与网络中其他症状的连接程度）。
最核心的症状是注意力难以集中、兴趣或愉悦感丧失、情绪低落和疲劳，不过每个症状的相对位置会因所考虑的中心性测量方法而略有不同。
与过去对重度抑郁症患者治疗的研究一致，当前研究结果支持这样一种模型，即抑郁症状在一个网络中存在因果联系，而非是一种共同潜在疾病的表现。文中讨论了这些发现的研究和临床意义，例如开发针对最核心症状的治疗方法。
