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. 2025 Oct;70(10):768-781.
doi: 10.1177/07067437251322399. Epub 2025 Mar 13.

Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l'outil PETRUSHKA

Edoardo G Ostinelli  1   2   3 , Matt Jaquiery  4 , Qiang Liu  1   2   5 , Rania Elgarf  1   2 , Nyla Haque  1   2 , Jennifer Potts  1   2 , Zhenpeng Li  1   2 , Orestis Efthimiou  6 , Sarah Markham  7 , Roger Ede  3 , Laurence Wainwright  1 , Karen Barros Parron Fernandes  8   9 , Bianca Barros Parron Fernandes  8 , Paulo Victor Carpaneze Dalaqua  8 , Anneka Tomlinson  1   2   3 , Katharine A Smith  1   2   3 , Caroline Zangani  1   2   3 , Franco De Crescenzo  1   2 , Marcos Liboni  10 , Benoit H Mulsant  11   12 , Andrea Cipriani  1   2   3 ; PETRUSHKA Team
Affiliations

Personalising Antidepressant Treatment for Unipolar Depression Combining Individual Choices, Risks and big Data: The PETRUSHKA Tool: Personnalisation du traitement antidépresseur de la dépression unipolaire associant choix individuels, risques et mégadonnées: l'outil PETRUSHKA

Edoardo G Ostinelli et al. Can J Psychiatry. 2025 Oct.

Abstract
in English, French

Objective: We 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).

Methods: The 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.

Results: We 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.

Conclusions: Using 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.Plain Language Summary TitleTailoring antidepressant treatment to individual patients with depression: the PETRUSHKA tool.

Objectif:: Nous résumons les étapes principales de l’élaboration et de l’évaluation d’un outil en ligne novateur, fondé sur des données probantes, qui soutient la prise de décision partagée dans le cadre des soins courants afin de personnaliser le traitement antidépresseur chez les adultes souffrant de dépression. L’outil PETRUSHKA fait partie de l’essai PETRUSHKA (Personalise antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs and big datA).

Méthodologie:: L’outil PETRUSHKA : (a) est basé sur des modèles de prédiction, qui utilisent à la fois des méthodes analytiques avancées, c.-à-d. des statistiques traditionnelles, et l’apprentissage automatique; (b) utilise les dossiers médicaux électroniques de patients recevant des soins primaires et souffrant de troubles dépressifs en Angleterre, ainsi que des données provenant d’essais cliniques randomisés sur les antidépresseurs utilisés pour traiter la dépression, tant au niveau global qu’au niveau individuel; (c) intègre les préférences des patients et des cliniciens (notamment en ce qui concerne les événements indésirables); (d) génère une liste hiérarchisée de recommandations de traitement personnalisé afin de guider le dialogue entre les cliniciens et les patients et de faciliter le choix du traitement final. L’outil PETRUSHKA repose sur une application Web, accessible à partir de n’importe quel ordinateur, téléphone intelligent ou tablette.

Résultats:: Nous avons utilisé un algorithme sur mesure pour déterminer le meilleur antidépresseur pour chaque patient, en utilisant les caractéristiques cliniques et démographiques des patients et en exploitant la puissance des innovations relatives à la technologie numérique, aux grands jeux de données et à l’apprentissage automatique. Nous avons créé un groupe de représentants de patients qui ont été impliqués dans la coproduction de l’outil, afin de maximiser son impact dans la pratique clinique réelle partout dans le monde. Pour tester l’outil, nous avons conçu un essai randomisé international multisites (échantillon visé : 504 participants), comparant l’outil PETRUSHKA aux soins habituels pour personnaliser le traitement pharmacologique des patients souffrant de trouble dépressif au Brésil, au Canada et au Royaume-Uni.

Conclusions:: L’utilisation d’outils d’aide à la décision pour les patients, fondés sur des données probantes, a été recommandée pour soutenir la prise de décision partagée, lorsque la qualité est assurée. Les futures études sur la médecine de précision en santé mentale devraient servir à mettre au point des outils en ligne multimodaux, en tenant compte des préférences des patients et de leurs caractéristiques démographiques, culturelles, cliniques et génétiques.

Keywords: adult psychiatry; antidepressants; caregivers; clinical trials; depressive disorders; evidence-based medicine; pharmacotherapy.

Plain language summary

Antidepressants are one of the main treatments for depression. Many patients, however, are given antidepressants, which prove ineffective or cause stressful side effects for them as individuals. This happens because antidepressants are prescribed without a clear understanding of which drug is the most appropriate medication for each patient. Regulatory bodies and guidelines developers have recommended prioritizing the improvement of antidepressant treatment for depression, but this advice has not yet been translated into practice. We suggest we already have sufficient evidence to distinguish between treatments according to personal characteristics, and the preferences and values of patients themselves. People with a diagnosis of depression often need additional support during the consultation visit when they make decisions about starting a new course of treatment. By a more careful analysis of existing data, we can better tailor the choice of a specific drug to a specific person (“personalized medicine”), to increase the chances that the drug will be tolerable and effective. We developed the PETRUSHKA tool, an evidence-based online system which will help doctors and patients together choose the best antidepressant for each individual with moderate to severe symptoms of depression. For the first time, this system will bring together the best available scientific information with the preferences of patients to provide a bespoke clinical decision aid for antidepressant treatment. The PETRUSHKA tool will be tested in a scientifically sound study of depressed patients who are to be treated with antidepressants in both primary and secondary care across Brazil, Canada and the UK. During the project, patients and carers have been involved in the co-development of the PETRUSHKA tool, which provides a model that can be extended to non-pharmacological treatments and to other psychiatric and non-psychiatric disorders, such as schizophrenia, diabetes and epilepsy.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Edoardo G. Ostinelli has received consultancy fees from Angelini Pharma. Franco De Crescenzo was supported by the NIHR Research Professorship to Professor Andrea Cipriani (grant RP-2017-08-ST2–006) and by the NIHR Oxford Health Biomedical Research Centre (grant BRC-1215-20005) and he is now an employee of Boehringer-Ingelheim International. Benoit Mulsant holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives or has received within the past five years research support from Brain Canada, the Canadian Institutes of Health Research, the CAMH Foundation, the Patient-Centered Outcomes Research Institute (PCORI), the US National Institute of Health (NIH), Capital Solution Design LLC (software used in a study funded by CAMH Foundation), and HAPPYneuron (software used in a study funded by Brain Canada). He has also been an unpaid consultant to Myriad Neuroscience. Anneka Tomlinson has received research, educational and consultancy fees from the Italian Network for Paediatric Trials (INCiPiT), Angelini Pharma, and Takeda and acted as a clinical advisor for Akrivia Health. Andrea Cipriani has received research, educational and consultancy fees from INCiPiT (Italian Network for Paediatric Trials), CARIPLO Foundation, Lundbeck and Angelini Pharma. All other authors declare no conflicts of interest.

Figures

Figure 1.
Figure 1.
Model pipeline for the backend of the algorithm (PETRUSHKA tool)—see text for full details.
Figure 2.
Figure 2.
Clinicians can log in to the PETRUSHKA tool web app, accessing patients from their clinic, either registered by themselves or with their colleagues. After the correct patient is selected, clinicians can optionally exclude some antidepressants based on the patient's medical history and ongoing medications.
Figure 3.
Figure 3.
The clinician and the patient will simultaneously access the PETRUSHKA tool from any internet-connected device. In real-time, the patient will be in charge of answering several questions: whether they would like to provide additional information on their preferences about side effects and which side effects are more important for them to avoid.
Figure 4.
Figure 4.
Three antidepressants are selected by the PETRUSHKA tool based on the patient individual characteristics and their preferences on side effects (if available). The five blue dots visually represent how strongly the PETRUSHKA tool is suggesting a specific antidepressant, jointly taking into consideration efficacy, acceptability, and the selected side effects. A breakdown of the selected five side effects for the three antidepressants is available both in numbers and depicted as bars. To avoid information flooding, predictions on all the 30 adverse events are available exclusively on the clinician screen, as additional information. Once an antidepressant is selected, the name is disclosed together with additional information on how to start it.
See this image and copyright information in PMC

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