McIntyre Roger S, Alda Martin, Baldessarini Ross J, Bauer Michael, Berk Michael, Correll Christoph U, Fagiolini Andrea, Fountoulakis Kostas, Frye Mark A, Grunze Heinz, Kessing Lars V, Miklowitz David J, Parker Gordon, Post Robert M, Swann Alan C, Suppes Trisha, Vieta Eduard, Young Allan, Maj Mario
Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada.
Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
World Psychiatry. 2022 Oct;21(3):364-387. doi: 10.1002/wps.20997.
Bipolar disorder is heterogeneous in phenomenology, illness trajectory, and response to treatment. Despite evidence for the efficacy of multimodal-ity interventions, the majority of persons affected by this disorder do not achieve and sustain full syndromal recovery. It is eagerly anticipated that combining datasets across various information sources (e.g., hierarchical "multi-omic" measures, electronic health records), analyzed using advanced computational methods (e.g., machine learning), will inform future diagnosis and treatment selection. In the interim, identifying clinically meaningful subgroups of persons with the disorder having differential response to specific treatments at point-of-care is an empirical priority. This paper endeavours to synthesize salient domains in the clinical characterization of the adult patient with bipolar disorder, with the overarching aim to improve health outcomes by informing patient management and treatment considerations. Extant data indicate that characterizing select domains in bipolar disorder provides actionable information and guides shared decision making. For example, it is robustly established that the presence of mixed features - especially during depressive episodes - and of physical and psychiatric comorbidities informs illness trajectory, response to treatment, and suicide risk. In addition, early environmental exposures (e.g., sexual and physical abuse, emotional neglect) are highly associated with more complicated illness presentations, inviting the need for developmentally-oriented and integrated treatment approaches. There have been significant advances in validating subtypes of bipolar disorder (e.g., bipolar I vs. II disorder), particularly in regard to pharmacological interventions. As with other severe mental disorders, social functioning, interpersonal/family relationships and internalized stigma are domains highly relevant to relapse risk, health outcomes, and quality of life. The elevated standardized mortality ratio for completed suicide and suicidal behaviour in bipolar disorder invites the need for characterization of this domain in all patients. The framework of this paper is to describe all the above salient domains, providing a synthesis of extant literature and recommendations for decision support tools and clinical metrics that can be implemented at point-of-care.
双相情感障碍在临床表现、疾病轨迹和治疗反应方面具有异质性。尽管有证据表明多模式干预有效,但大多数受该疾病影响的人并未实现并维持完全的综合征康复。人们热切期待,整合来自各种信息源的数据集(例如分层的“多组学”测量、电子健康记录),并使用先进的计算方法(例如机器学习)进行分析,将为未来的诊断和治疗选择提供信息。在此期间,在临床护理点识别出对特定治疗有不同反应的该疾病患者的具有临床意义的亚组是一项经验性优先事项。本文致力于综合成年双相情感障碍患者临床特征中的显著领域,其总体目标是通过为患者管理和治疗考量提供信息来改善健康结果。现有数据表明,对双相情感障碍的特定领域进行特征描述可提供可操作的信息并指导共同决策。例如,已确凿证实,混合特征的存在——尤其是在抑郁发作期间——以及身体和精神共病情况会影响疾病轨迹、治疗反应和自杀风险。此外,早期环境暴露(例如性虐待和身体虐待、情感忽视)与更复杂的疾病表现高度相关,这就需要采用以发展为导向的综合治疗方法。在双相情感障碍亚型(例如双相I型与双相II型障碍)的验证方面已经取得了重大进展,特别是在药物干预方面。与其他严重精神障碍一样,社会功能、人际关系/家庭关系和内化耻辱感是与复发风险、健康结果和生活质量高度相关的领域。双相情感障碍中因自杀死亡和自杀行为导致的标准化死亡率升高,这就需要对所有患者的这一领域进行特征描述。本文的框架是描述上述所有显著领域,综合现有文献,并为可在临床护理点实施的决策支持工具和临床指标提供建议。