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  3. 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.
 

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.

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BORIS DOI
10.48620/87249
Date of Publication
March 13, 2025
Publication Type
Article
Division/Institute

Institute of General ...

Contributor
Ostinelli, Edoardo G
Jaquiery, Matt
Liu, Qiang
Elgarf, Rania
Haque, Nyla
Potts, Jennifer
Li, Zhenpeng
Efthimiou, Orestisorcid-logo
Institute of General Practice and Primary Care (BIHAM)
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
Subject(s)

600 - Technology::610...

Series
The Canadian Journal of Psychiatry
ISSN or ISBN (if monograph)
1497-0015
0706-7437
Publisher
Canadian Psychiatric Association
Language
English
Publisher DOI
10.1177/07067437251322399
PubMed ID
40079809
Uncontrolled Keywords

adult psychiatry

antidepressants

caregivers

clinical trials

depressive disorders

evidence-based medici...

pharmacotherapy

Description
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.Tailoring antidepressant treatment to individual patients with depression: the PETRUSHKA toolPlain Language SummaryAntidepressants 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.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.
Handle
https://boris-portal.unibe.ch/handle/20.500.12422/206732
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Ostinelli CanJPsychiatry 2025.pdftextAdobe PDF3.16 MBpublishedOpen
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