Maj Mario, Stein Dan J, Parker Gordon, Zimmerman Mark, Fava Giovanni A, De Hert Marc, Demyttenaere Koen, McIntyre Roger S, Widiger Thomas, Wittchen Hans-Ulrich
Department of Psychiatry, University of Campania "L. Vanvitelli", Naples, Italy.
South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa.
World Psychiatry. 2020 Oct;19(3):269-293. doi: 10.1002/wps.20771.
Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial-and-error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta-analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self-report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above-mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.
抑郁症被广泛认为是一种异质性疾病，人们一再强调需要进一步明确已确诊患者的个体特征，以便制定个性化的治疗方案。然而，目前指导个性化治疗的研究证据并不完整，治疗方案的选择通常基于临床医生和/或患者的偏好以及安全性问题，采用反复试验的方式，很少关注具体病例的特殊特征。这可能是大多数抑郁症患者在接受首次治疗后未能实现缓解的原因之一。最近，对临床试验数据库进行的一些二次分析，如个体患者数据荟萃分析和机器学习，缓和了人们对抑郁症治疗个性化在常规临床实践中实际可行性的主要悲观态度，这些分析表明某些变量确实有助于识别可能对各种抗抑郁药物、抗抑郁药物与特定心理治疗有不同反应的患者。最近再次强调了开发指导抑郁症治疗个性化的决策支持工具的必要性，并指出这些工具应通过使用一系列全面的自我报告和临床测量方法的大型观察性研究来开发。本文旨在系统地描述在抑郁症治疗个性化过程中应考虑的显著领域。对于每个领域，总结了现有的研究证据，并对相关评估工具进行了综述，特别关注它们在常规临床实践中的适用性，同时也考虑到它们可能被纳入上述综合测量方法中。强调了该领域研究应解决的主要未满足需求。在现有证据允许为临床医生提供可立即用于使抑郁症管理更个性化的具体建议的地方，突出显示了这些建议。事实上，本文的一些章节，如关于神经认知和身体合并症的章节，表明抑郁症的现代管理正变得越来越复杂，除了简单地选择抗抑郁药物和/或心理治疗外，还有几个组成部分，其中一些已经可以可靠地实现个性化。
