Introduction Matching treatment to specific patients is too often a matter of trial and error, while treatment efficacy should be optimised by limiting risks and costs and by incorporating patients’ preferences. Factors influencing an individual’s drug response in major depressive disorder may include a number of clinical variables (such as previous treatments, severity of illness, concomitant anxiety etc) as well demographics (for instance, age, weight, social support and family history). Our project, funded by the National Institute of Health Research, is aimed at developing and subsequently testing a precision medicine approach to the pharmacological treatment of major depressive disorder in adults, which can be used in everyday clinical settings. Methods and analysis We will jointly synthesise data from patients with major depressive disorder, obtained from diverse datasets, including randomised trials as well as observational, real-world studies. We will summarise the highest quality and most up-to-date scientific evidence about comparative effectiveness and tolerability (adverse effects) of antidepressants for major depressive disorder, develop and externally validate prediction models to produce stratified treatment recommendations. Results from this analysis will subsequently inform a web-based platform and build a decision support tool combining the stratified recommendations with clinicians and patients’ preferences, to adapt the tool, increase its’ reliability and tailor treatment indications to the individual-patient level. We will then test whether use of the tool relative to treatment as usual in real-world clinical settings leads to enhanced treatment adherence and response, is acceptable to clinicians and patients, and is economically viable in the UK National Health Service. Discussion This is a clinically oriented study, coordinated by an international team of experts, with important implications for patients treated in real-world setting. This project will form a test-case that, if effective, will be extended to non-pharmacological treatments (either face-to-face or internet-delivered), to other populations and disorders in psychiatry (for instance, children and adolescents, or schizophrenia and treatment-resistant depression) and to other fields of medicine.

中文翻译：

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结合个人选择，风险和大数据（PETRUSHKA），个性化针对单相抑郁的抗抑郁药治疗：原理和方案。

简介与特定患者匹配治疗常常是反复试验的问题，而应通过限制风险和成本并结合患者的喜好来优化治疗效果。影响个人在重度抑郁症中的药物反应的因素可能包括许多临床变量（例如以前的治疗，疾病的严重程度，伴随的焦虑等）以及人口统计学（例如年龄，体重，社会支持和家族史）。我们的项目由美国国立卫生研究院（National Institute of Health Research）资助，旨在开发并随后测试一种精密的医学方法，以药物治疗成年人的重性抑郁症，该方法可用于日常临床环境中。方法和分析我们将联合综合来自重度抑郁症患者的数据，从各种数据集获得，包括随机试验以及观察性，现实世界研究。我们将总结有关抗抑郁药对重性抑郁症的相对有效性和耐受性（不良影响）的最高质量和最新科学证据，并开发和从外部验证预测模型以产生分层的治疗建议。该分析的结果随后将为基于Web的平台提供信息，并构建将分层建议与临床医生和患者喜好相结合的决策支持工具，以适应该工具，提高其可靠性并根据个人患者水平调整治疗适应症。然后，我们将测试在现实世界的临床环境中照常使用与治疗相关的工具是否会增强治疗依从性和反应性，临床医生和患者都可以接受，并且在英国国家卫生局（National Health Service）中在经济上可行。讨论这是一项临床研究，由国际专家团队进行协调，对在现实世界中接受治疗的患者具有重要意义。该项目将构成一个测试案例，如果有效的话，将扩展到非药物治疗（面对面或通过互联网提供），精神病学的其他人群和疾病（例如，儿童和青少年）或精神分裂症和抗药性抑郁症）和其他医学领域。对在现实世界中接受治疗的患者具有重要意义。该项目将构成一个测试案例，如果有效的话，将扩展到非药物治疗（面对面或通过互联网提供），精神病学的其他人群和疾病（例如，儿童和青少年）或精神分裂症和难治性抑郁症）和其他医学领域。对在现实世界中接受治疗的患者具有重要意义。该项目将构成一个测试案例，如果有效的话，将扩展到非药物治疗（面对面或通过互联网提供），精神病学的其他人群和疾病（例如，儿童和青少年）或精神分裂症和难治性抑郁症）和其他医学领域。