Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol
Open accessAbstract
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.
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The PETRUSHKA initiative employed network meta-analysis to generate antidepressant efficacy rankings incorporating patient-specific data and preference weighting.
“The PETRUSHKA initiative employed network meta-analysis to generate antidepressant efficacy rankings incorporating patient-specific data and preference weighting”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Tomlinson et al., 2020
The Methods and analysis section (Phase I) proposes using individual-patient-data network meta-regression to predict antidepressant outcomes from patient characteristics. Phase 2 proposes rankings based on those predictions and patients’ preferences. This is a protocol describing planned work, not evidence that PETRUSHKA had already employed the method or generated the rankings. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFormulation aids in reaching the ultimate goal of shared or supported decision-making through identifying the person's values or preferences as well as the factors informing a clinician's personalised treatment recommendations.
“Formulation aids in reaching the ultimate goal of shared or supported decision-making through identifying the person's values or preferences as well as the factors informing a clinician's personalised treatment recommendations (Panel 2). 603”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [603]
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Time for united action on depression: a Lancet–World Psychiatric Association Commission
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- Authors
- Tomlinson, A., Furukawa, T. A., Efthimiou, O., Salanti, G., De Crescenzo, F., Singh, I., & Cipriani, A.
- Affiliations
- University of Oxford (GB)
- Kyoto University (JP)
- University of Bern (CH)
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust (GB)
- Corresponding author
- Cipriani, Andrea
- ORCID
- Venue
- Evidence Based Mental Health
- Publisher
- BMJ
- Published
- 2019-10-23
- Volume
- 23
- Issue
- 2
- Pages
- 52-56
- ISSN
- 1362-0347 (ISSN-L)
- 1468-960X
- PMC
- PMC7229905
- OpenAlex
- W2981500465
- Cited by
- 54
- References
- 35
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 13 · 2023: 7 · 2022: 9 · 2021: 9 · 2020: 9 · 2019: 1
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Evidence-Based Mental Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Open Access CRIS of the University of Bern
- Funders
- National Institute for Health Research (BRC-1215-20005)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- major depressive disorder, antidepressants, precision medicine, personalized treatment, treatment response prediction, comparative effectiveness, adverse drug reactions, decision support system, patient preferences, medication adherence, real-world clinical practice, economic evaluation, big data, Petrushka
- MeSH terms
- Clinical Studies as Topic, Adult, Antidepressive Agents, Decision Support Techniques, Major Depressive Disorder, Humans, Precision Medicine, Datasets as Topic
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