Psych-STRATA - A Stratified Treatment Algorithm in Psychiatry: A program on stratified pharmacogenomics in severe mental illness

精神健康的一个关键问题是，多达三分之一的严重精神障碍患者对药物治疗产生抵抗力。然而，出现早期治疗耐药迹象的患者没有得到足够的早期强化药物治疗，而是接受了一种循序渐进的反复试验治疗方法。这种情况源于三个主要的知识和翻译差距：i)我们缺乏有效的方法来在疾病进程的早期识别有TRR风险的个人，II。)我们缺乏有效的、个性化的治疗策略，其基础是对TR和III的生物学基础的洞察。)我们缺乏有效的程序来将关于TR的科学见解转化为临床实践、初级保健和治疗指南。心理学的中心目标是弥合这些差距，为转变为治疗决策过程量身定做的治疗决策过程铺平道路，这些过程是为有风险的患者量身定做的。为此，我们的目标是建立基于证据的标准，以决定对精神分裂症、双相情感障碍和严重抑郁症等主要精神障碍的TR高危个体进行早期强化治疗。心理层面我会。)根据对史无前例的遗传、生物、数字心理健康和临床数据的综合分析，剖析再生障碍性贫血的生物学基础，并建立标准，以便能够及早发现易患再生障碍性贫血的个体。II.)此外，我们将根据泛欧洲在SCZ、BD和MDD的临床试验，在治疗过程的早期确定针对TRR高危个体的有效治疗策略。这些努力将使建立新的多模式机器学习模型来预测TR风险和治疗反应成为可能。最后，三.)我们将通过建立个性化治疗决策支持和以患者为导向的决策精神健康委员会的集成原型，使这些发现能够转化为临床实践。

A key problem in Mental Health is that up to one third of patients suffering from major mental disorders develop resistance against drug therapy. However, patients showing early signs of treatment resistance (TR) do not receive adequate early intensive pharmacological treatment but instead they undergo a stepwise trial-and-error treatment approach. This situation originates from three major knowledge and translation gaps: i.) we lack effective methods to identify individuals at risk for TR early in the disease process, ii.) we lack effective, personalized treatment strategies grounded in insights into the biological basis of TR, and iii.) we lack efficient processes to translate scientific insights about TR into clinical practice, primary care and treatment guidelines. It is the central goal of PSYCH-STRATA to bridge these gaps and pave the way for a shift towards a treatment decision-making process tailored for the individual at risk for TR. Tothat end, we aim to establish evidence-based criteria to make decisions of early intense treatment in individuals at risk for TR across the major psychiatric disorders ofschizophrenia, bipolar disorder and major depression. PSYCH-STRATA will i.) dissect the biological basis of TR and establish criteria to enable early detection of individuals at risk for TR based on the integrated analysis of an unprecedented collection of genetic, biological, digital mental health, and clinical data. ii.) Moreover, we will determine effective treatment strategies of individuals at risk for TR early in the treatment process, based on pan-European clinical trials in SCZ, BD and MDD. These efforts will enable the establishment of novel multimodal machine learning models to predict TR risk and treatment response. Lastly, iii.) we will enable the translation of these findings into clinical practice by prototyping the integration of personalized treatment decision supportand patient-oriented decision-making mental health boards.