Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK. ciaran.o'
Centre for Outcomes Research and Effectiveness (CORE), Research Department of Clinical, Educational & Health Psychology, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
BMC Med. 2021 May 6;19(1):109. doi: 10.1186/s12916-021-01971-0.
Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.
Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.
Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.
The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.
抑郁症通常被认为是一种具有多种潜在治疗选择的单一潜在疾病。然而，患有重度抑郁症的患者在症状表现和合并症方面差异很大，例如焦虑症。治疗结果也存在很大差异，一些焦虑合并症与预后较差相关，但对其原因的了解有限，也没有多少信息可以指导抑郁症的临床管理。我们需要更好地了解抑郁症，包括焦虑合并症，并考虑将广泛的症状与治疗结果联系起来。
使用六项针对抑郁患者的 RCT 的个体患者数据（总 n=2858），使用个体项目和总分来估计症状对三个干预后时间点的结果的不同影响。使用症状网络（图形高斯模型）来探索抑郁和焦虑症状之间的功能关系，并比较治疗缓解者和持续症状者的网络，以确定潜在的预后指标。
在预测 3 至 4 个月和 6 至 8 个月的结果时，项目级预测与总分相似，但在预测 9 至 12 个月的结果时优于总分。在这些时间点，悲观情绪成为最重要的预测症状（相对于所有其他症状）。在研究开始时的网络结构中，症状聚类为身体症状、认知症状和焦虑症状。悲伤、悲观和犹豫不决充当了社区之间的桥梁，而悲伤和失败/无价值感是最核心（即相互关联）的症状。研究开始时网络的连接性对于未来的缓解者和持续症状者没有差异。
特定症状与结果的关联的相对重要性以及网络内的相互作用突出了对症状进行跨诊断评估和制定的价值，这对治疗和预后都有帮助。我们讨论了使用补充统计方法来提高对精神病理学的理解的潜力。
