Ostinelli Edoardo G, Jaquiery Matt, Liu Qiang, Elgarf Rania, Haque Nyla, Potts Jennifer, Li Zhenpeng, Efthimiou Orestis, Markham Sarah, Ede Roger, Wainwright Laurence, Fernandes Karen Barros Parron, Fernandes Bianca Barros Parron, Dalaqua Paulo Victor Carpaneze, Tomlinson Anneka, Smith Katharine A, Zangani Caroline, De Crescenzo Franco, Liboni Marcos, Mulsant Benoit H, Cipriani Andrea
Department of Psychiatry, University of Oxford, Oxford, UK.
Oxford Precision Psychiatry Lab, National Institute for Health Research Oxford Health Biomedical Research Centre, University of Oxford, Oxford, UK.
Can J Psychiatry. 2025 Mar 13:7067437251322399. doi: 10.1177/07067437251322399.
ObjectiveWe summarize the key steps to develop and assess an innovative online, evidence-based tool that supports shared decision-making in routine care to personalize antidepressant treatment in adults with depression. This PETRUSHKA tool is part of the PETRUSHKA trial (Personalize antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs, and big datA).MethodsThe PETRUSHKA tool: (a) is based on prediction models, which use a combination of advanced analytics, i.e., traditional statistics, and machine learning methods; (b) utilizes electronic health records from primary care patients with depressive disorder in England and data from randomized controlled trials on antidepressants in depression, both at aggregate and individual patient level; (c) incorporates preferences from patients and clinicians (especially about adverse events); (d) generates a ranked list of personalized treatment recommendations to inform the discussion between clinicians and patients, and facilitates the final treatment choice. The PETRUSHKA tool is implemented as a web-based application, accessible from any computer, smartphone or tablet.ResultsWe employed a bespoke algorithm to identify the best antidepressant for each individual patient, using patients' clinical and demographic characteristics and harnessing the power of innovations in digital technology, large datasets and machine learning. We established a dedicated group of patient representatives that were involved in the co-production of the tool, to maximize its impact in real-world clinical practice across the world. To test the tool, we designed an international multi-site, randomized trial (target sample: 504 participants), comparing the PETRUSHKA tool with usual care to personalize pharmacological treatment in patients with depressive disorder across Brazil, Canada and the UK.ConclusionsUsing evidence-based patient decision aids has been recommended to support shared decision-making when quality is assured. Future studies in precision mental health should develop multimodal web tools, incorporating patients' preferences and their individual demographic, cultural, clinical, and genetic characteristics.
目的
我们总结了开发和评估一种创新的在线循证工具的关键步骤，该工具支持在常规护理中进行共同决策，以实现成人抑郁症患者抗抑郁治疗的个性化。这种PETRUSHKA工具是PETRUSHKA试验（用于单相抑郁症的个性化抗抑郁治疗，结合个体选择、风险和大数据）的一部分。
方法
PETRUSHKA工具：(a) 基于预测模型，该模型使用传统统计和机器学习方法等先进分析方法的组合；(b) 利用来自英国初级护理抑郁症患者的电子健康记录以及抑郁症抗抑郁药物随机对照试验的数据，包括总体和个体患者层面的数据；(c) 纳入患者和临床医生的偏好（特别是关于不良事件的偏好）；(d) 生成个性化治疗建议的排名列表，为临床医生和患者之间的讨论提供信息，并促进最终治疗选择。PETRUSHKA工具作为基于网络的应用程序实现，可从任何计算机、智能手机或平板电脑访问。
结果
我们采用了定制算法，利用患者的临床和人口统计学特征，并借助数字技术、大型数据集和机器学习创新的力量，为每个患者确定最佳抗抑郁药物。我们成立了一个专门的患者代表小组，参与该工具的共同制作，以最大限度地提高其在全球实际临床实践中的影响力。为了测试该工具，我们设计了一项国际多中心随机试验（目标样本：504名参与者），将PETRUSHKA工具与常规护理进行比较，以实现巴西、加拿大和英国抑郁症患者药物治疗的个性化。
结论
当质量得到保证时，建议使用循证患者决策辅助工具来支持共同决策。精准心理健康的未来研究应开发多模式网络工具，纳入患者的偏好及其个体人口统计学、文化、临床和遗传特征。
