The PETRUSHKA study is a Randomised Controlled Trial (RCT) that aims to personalise pharmacological treatment for adults with major depressive disorder in the NHS.
PETRUSHKA (Personalise antidEpressant Treatment foR Unipolar depreSsion combining individual cHoices, risKs and big datA)
What is the PETRUSHKA study?
The PETRUSHKA study is a Randomised Controlled Trial (RCT) that aims to personalise pharmacological treatment for adults with major depressive disorder in the NHS. It uses a web-based decision support tool (the PETRUSHKA tool), which combines the best available evidence with patients’ and clinicians’ preferences, to support the shared decision making process and predict which antidepressant works best for each individual patient.
The study will investigate whether a personalised approach is better than usual care in terms of adherence to antidepressant treatment, clinical response, and quality of life for patients seen within primary care in the UK.
About 80% of people identified as suffering from a depressive disorder in primary care in the UK receive an antidepressant prescription in the first year of diagnosis. Guidelines on the length of treatment with antidepressants recommend at least 6-8 weeks before changing the medication. However, the majority of the prescriptions in the UK are for less than 30 days.
Two of the most important factors for short treatment durations are the initial side effects of the medication and their perceived marginal efficacy. Personalisation of treatment could improve adherence to treatment, especially when also taking into account individual preferences, to help choose the right antidepressant for each individual patient.
In this single-blind RCT, 504 participants are being recruited through primary care practices across the UK (252 randomised to the PETRUSHKA tool and 252 to usual care). Inclusion criteria include: male or female NHS patients aged 18 to 74 years inclusive, with a diagnosis of a depressive disorder who require treatment with antidepressant as monotherapy.
All antidepressants included in the PETRUSHKA tool are currently licensed for use in the UK, therefore no additional risk to patients is expected.
This study is being funded by the National Institute for Health and Care Research (NIHR). This project is expected to run until Autumn 2023.
How to get involved
Recent publications related to PETRUSHKA trial
Protocol and rationale:
- Tomlinson A, Furukawa TA, Efthimiou O, Salanti G, De Crescenzo F, Singh I, Cipriani A. Personalise antidepressant treatment for unipolar depression combining individual choices, risks and big data (PETRUSHKA): rationale and protocol. Evid Based Ment Health. 2020 May;23(2):52-56. doi: 10.1136/ebmental-2019-300118. Epub 2019 Oct 23. PMID: 31645364; PMCID: PMC7229905.
- O'Dell B, Stevens K, Tomlinson A, Singh I, Cipriani A. Building trust in artificial intelligence and new technologies in mental health. Evid Based Ment Health. 2022 May;25(2):45-46. doi: 10.1136/ebmental-2022-300489. Epub 2022 Apr 20. PMID: 35444002.
- Liu Q, Salanti G, De Crescenzo F, Ostinelli EG, Li Z, Tomlinson A, Cipriani A, Efthimiou O. Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression. BMC Psychiatry. 2022 May 16;22(1):337. doi: 10.1186/s12888-022-03986-0. PMID: 35578254; PMCID: PMC9112573.
- Maslej MM, Furukawa TA, Cipriani A, Andrews PW, Sanches M, Tomlinson A, Volkmann C, McCutcheon RA, Howes O, Guo X, Mulsant BH. Individual Differences in Response to Antidepressants: A Meta-analysis of Placebo-Controlled Randomized Clinical Trials. JAMA Psychiatry. 2021 May 1;78(5):490-497. doi: 10.1001/jamapsychiatry.2020.4564. PMID: 33595620; PMCID: PMC7890446.
- Hamza T, Furukawa TA, Orsini N, Cipriani A, Iglesias CP, Salanti G. A dose-effect network meta-analysis model with application in antidepressants using restricted cubic splines. Stat Methods Med Res. 2022 Feb 24:9622802211070256. doi: 10.1177/09622802211070256. Epub ahead of print. PMID: 35200062.
- Chevance A, Ravaud P, Tomlinson A, Le Berre C, Teufer B, Touboul S, Fried EI, Gartlehner G, Cipriani A, Tran VT. Identifying outcomes for depression that matter to patients, informal caregivers, and health-care professionals: qualitative content analysis of a large international online survey. Lancet Psychiatry. 2020 Aug;7(8):692-702. doi: 10.1016/S2215-0366(20)30191-7. Erratum in: Lancet Psychiatry. 2020 Sep;7(9):e59. PMID: 32711710.
- Liu Q, Vaci N, Koychev I, Kormilitzin A, Li Z, Cipriani A, Nevado-Holgado A. Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model. BMC Med. 2022 Feb 1;20(1):45. doi: 10.1186/s12916-022-02250-2. PMID: 35101059; PMCID: PMC8805393.
- Turner EH, Cipriani A, Furukawa TA, Salanti G, de Vries YA. Selective publication of antidepressant trials and its influence on apparent efficacy: Updated comparisons and meta-analyses of newer versus older trials. PLoS Med. 2022 Jan 19;19(1):e1003886. doi: 10.1371/journal.pmed.1003886. PMID: 35045113; PMCID: PMC8769343.
- De Crescenzo F, Garriga C, Tomlinson A, Coupland C, Efthimiou O, Fazel S, Hippisley-Cox J, Cipriani A. Real-world effect of antidepressants for depressive disorder in primary care: protocol of a population-based cohort study. Evid Based Ment Health. 2020 Aug;23(3):122-126. doi: 10.1136/ebmental-2020-300149. Epub 2020 Jun 18. PMID: 32554440.
- Gillett G, Tomlinson A, Efthimiou O, Cipriani A. Predicting treatment effects in unipolar depression: A meta-review. Pharmacol Ther. 2020 Aug;212:107557. doi: 10.1016/j.pharmthera.2020.107557. Epub 2020 May 8. PMID: 32437828.
- Vaci N, Liu Q, Kormilitzin A, De Crescenzo F, Kurtulmus A, Harvey J, O'Dell B, Innocent S, Tomlinson A, Cipriani A, Nevado-Holgado A. Natural language processing for structuring clinical text data on depression using UK-CRIS. Evid Based Ment Health. 2020 Feb;23(1):21-26. doi: 10.1136/ebmental-2019-300134. PMID: 32046989.
- Tomlinson A, Boaden K, Cipriani A. Withdrawal, dependence and adverse events of antidepressants: lessons from patients and data. Evid Based Ment Health. 2019 Nov;22(4):137-138. doi: 10.1136/ebmental-2019-300121. PMID: 31645408.
- Kernot C, Tomlinson A, Chevance A, Cipriani A. One step closer to personalised prescribing of antidepressants: using real-world data together with patients and clinicians' preferences. Evid Based Ment Health. 2019 Aug;22(3):91-92. doi: 10.1136/ebmental-2019-300105. Epub 2019 Jul 13. PMID: 31302612.
- Tomlinson A, Efthimiou O, Boaden K, New E, Mather S, Salanti G, Imai H, Ogawa Y, Tajika A, Kishimoto S, Kikuchi S, Chevance A, Furukawa TA, Cipriani A. Side effect profile and comparative tolerability of 21 antidepressants in the acute treatment of major depression in adults: protocol for a network meta-analysis. Evid Based Ment Health. 2019 May;22(2):61-66. doi: 10.1136/ebmental-2019-300087. Epub 2019 Apr 17. PMID: 30996028.
- Cipriani A, Tomlinson A. Providing the most appropriate care to our individual patients. Evid Based Ment Health. 2019 Feb;22(1):1-2. doi: 10.1136/ebmental-2019-300080. Epub 2019 Jan 21. PMID: 30665987.